BIM in safety-critical Facilites

BIM in Safety-Critical Facilities: Efficiency and Data Sovereignty as the Keys to Success, for Example in Defense Construction by Charlotte Remme | 01. August 2026 back to the post overview The turning point in security policy presents the German defense construction sector with a monumental historical challenge: Billions in investments from the special fund must be channeled as quickly as possible into modern barracks, depots, and security infrastructure. However, the reality on the ground often slows down this momentum, as outdated paper plans, unclear environmental liabilities, and complex security requirements cause massive delays in planning. This is precisely where digitalization fills a critical gap. Digital methods such as Building Information Management (BIM) provide the urgently needed transparency and structure. They transform analog defense construction into efficient, future-proof, and data-sovereign large-scale projects. The Unique Role and Importance of Defense Construction Security-critical facilities are not ordinary real estate: They must withstand extreme stresses, meet highly sensitive structural protection criteria (SIRA), and operate autonomously in an emergency. The reason this issue is so politically explosive right now is the unprecedented pressure to invest: Record sums have been budgeted for the maintenance of barracks and facilities. The defense budget is growing massively, supported by the extensive resources of the special fund and the new borrowing limits of the national infrastructure fund. However, the reality at these properties—many of which are decades old—is slowing down the digital transformation. For example, an estimated 70% of the approximately 380 active Bundeswehr sites lack up-to-date or usable as-built plans. Planners are thus faced with the challenge of coordinating highly complex renovations based on incomplete data, which leads to enormous cost increases without digital methods. These information gaps in building construction are compounded by unforeseeable civil engineering risks: missing or purely analog documentation regarding contaminated soil or unexploded ordnance regularly leads to construction halts and unclear renovation costs. Although the Federal Ministry of Defense (BMVg) is now an official partner of the BIM Deutschland initiative and the Construction Guidelines (BFR BIM) have provided the binding framework for years, actual implementation has stalled. Many projects are stuck in a transitional phase. While agencies such as the BAIUDBw and the BImA define BIM requirements, there is often a lack of standardized, tender-ready processes. The widespread use of the BIM method remains the exception for safety-critical structures. Challenges in Defense Construction The biggest hurdle in modernizing safety-critical properties lies in the combination of incomplete documentation and highly complex permitting processes. Because reliable plans do not exist for a large portion of the decades-old building stock, planners and authorities must painstakingly and time-consumingly reconstruct every step manually. The process also requires an immense amount of coordination, as federal regulations, state-specific building authorities, and the strict SIRA security requirements must all be managed simultaneously. Without a consistent digital foundation, those involved find themselves in a logistical dead end where planning errors are inevitable. If conventional planning methods are maintained in this highly sensitive environment, serious risks and construction halts threaten as the project progresses Without precise as-built documentation—for example, using Scan2BIM—unforeseen structural obstacles in existing buildings lead to major planning errors and cost overruns. The lack of a digital link between contaminated sites and unexploded ordnance regularly causes sudden construction stoppages. Without software-supported collision detection, physical conflicts arise on the construction site between highly sensitive building systems and shielded EMC/TEMPEST areas. If protection classes (such as access control or ballistic protection) are not directly located within the model, there is a risk of late, extremely costly corrective measures during construction. Without a security-compliant data platform, the use of commercial clouds poses a massive security risk to sensitive data. If there is no structured handover of facility management data to operators, this data gap leads to increased maintenance costs and unplanned outages of critical infrastructure. Scan2BIM: Using Laser Scanning for Precise As-Built Documentation Since the majority of security-critical properties in Germany lack up-to-date or reliable as-built documentation, modern 3D laser scanning provides the indispensable foundation for any modernization project. In the Scan2BIM process, the actual existing structure—such as barracks or depots—is captured in its entirety within a few days using high-precision laser scanners, with millimeter-level accuracy, in the form of a point cloud. This digital scan data is then used as a reference to create a digital information model that combines geometric structure and alphanumeric data. The BIM2Scan process results in: Significant time savings: Data capture and model conversion are completed within a few weeks instead of months. Valid geometric data: Prevention of delays and risks of cost overruns in the procurement process. Minimization of design errors: The error-free digital data foundation eliminates the risk of designing based on incorrect or outdated dimensions. Instead of spending months searching for outdated paper plans or risking inaccurate manual measurements, project participants thus have an error-free, digital design foundation available within a very short time. Hazard Mapping: Digitally Linking Contaminated Sites and Unexploded Ordnance Safety-critical facilities often harbor unforeseeable underground risks, such as unexploded ordnance, fuel contamination, or asbestos in older buildings. In conventional construction, these unclear contamination issues regularly lead to sudden construction halts, skyrocketing remediation costs, and legal liability risks, as information is often available only in analog form or is incomplete. The BIM method solves this problem through intelligent 3D hazard mapping: Here, contamination and hazard zones are directly linked to the digital building model as visual exclusion zones and risk attributes. This allows planners and contractors to see exactly what hazards lurk in the ground or within the building structure even before the first shovel hits the ground. This intelligent integration delivers measurable benefits in practice: By visualizing explosive ordnance and hazard zones in the model at an early stage, earthwork and remediation can be scheduled proactively. Remediation and disposal costs can be calculated precisely. Comprehensive digital documentation of contaminated sites protects clients and planners from legal liability risks. The visual and data-driven presentation accelerates approvals by the relevant environmental and safety authorities. “Project experience shows that the use of digital hazard mapping

BIM and laser scanning in hospital operations: Digital as-built surveys as the key to safe refurbishment

BIM and laser scanning in hospital operations: Digital as-built surveys as the key to safe refurbishment by Kai Weist & Sarah Zonsius | May 26th, 2026 back to the overview page Hospitals are never truly finished; they are constantly being transformed, extended and modernised. And this always takes place under a condition that is not common in conventional construction: whilst the hospital remains in operation. This circumstance makes hospital construction one of the most complex disciplines in building construction and, at the same time, presents a unique opportunity for digital methods. BIM and 3D laser scanning can make a decisive contribution here: as the basis for reliable planning, transparent coordination and efficient digital hospital operations. Hospital construction: a special case in building construction Hardly any other type of building combines so many requirements within such a confined space as a hospital. Operating theatres, intensive care units, laboratories, central sterile supply departments and medical technology infrastructure result in an exceptionally high density of building services. Redundant power supplies, medical gases, cleanroom technology and highly specialised ventilation systems are closely interlinked, meaning that coordination conflicts arise not in the architectural design but within the technical framework. Added to this is structural pressure: the investment backlog in German hospitals is estimated at between 30 and 50 billion euros. With the hospital reform and the transformation fund of over 50 billion euros (2026–2035), the pressure to act is increasing significantly. Refurbishments, extensions and new builds must be completed more quickly than ever before, often whilst the hospital is operating at full capacity. The Federal Institute for Research on Building, Urban Affairs and Spatial Development (BBSR) has addressed this issue through the KlinikBIM research project: a 169-page guide produced by RWTH Aachen University sets out, for the first time, a robust standard for BIM implementation in hospital construction; this standard defines guidelines ranging from the client’s information requirements (AIA) through to integration into day-to-day operations. Building whilst operations continue: a particular challenge What is considered the exception in other sectors is the norm in hospital construction: refurbishment and extension work takes place whilst patients are being cared for in adjacent areas. This places exceptional demands on planning, coordination and execution: Noise and dust control must be strictly monitored, as construction dust can increase the risk of infection and compromise hygiene barriers. Shutdown periods must be planned precisely: short time slots, often at night or at weekends, require meticulous coordination in advance. Logistics within the existing building are complex: transport routes, storage areas and access points must be coordinated with clinical operations. Infection control measures (e.g. ICRA – Infection Control Risk Assessment) must be taken into account in every trade. Scanning activities and site inspections must be planned in such a way as to ensure patient safety and data security.   Missing or outdated as-built documentation makes all of this even more difficult. If the actual location of an exhaust air duct is unknown or a drainage system is missing from the plans, surprises arise on site which have direct consequences for deadlines, costs and clinical operations. “In hospital construction, any delay is more than just a financial problem – it can have a direct impact on medical procedures.” Kai Weist Laser scanning in hospital settings: Digital reality capture without disrupting operations 3D laser scanning is a measurement technology in which a rotating laser beam captures millions of measurement points in a very short space of time, generating a highly accurate three-dimensional point cloud of the actual building. This method offers significant advantages in hospital operations: Minimal disruption to operations: Scans can be carried out during night shifts or during quieter periods of operation. Modern laser scanners can capture entire corridors and plant rooms in a matter of minutes. As-built survey with millimetre accuracy: Hospitals have often undergone refurbishments over decades, which have rarely been fully documented. The laser scan provides the actual current condition, regardless of what is shown on the plans. Mapping of complex building services systems: Laser scanning really comes into its own with highly dense technical installations (ventilation, medical gas, IT cabling, fire protection), revealing collisions, spare space and the actual positions of components. Data protection-compliant operation: Professional scanning service providers work to clearly defined protocols to ensure that no patient-related data is recorded.   Compared with conventional manual surveys, laser scanning reduces the time and effort required for data collection by up to 60 per cent. For large hospital buildings, which would otherwise take months to survey, this represents a significant economic advantage. Scan2BIM: From laser scan to structured as-built model The next step after the as-built survey is modelling: the point cloud is used to create a structured BIM model – the so-called Scan2BIM process. In this process, the point cloud is used as a reference to create a digital information model that combines geometric structure and alphanumeric data. For hospital construction, a usage-oriented approach is crucial: not every area of a hospital requires the same level of modelling detail. In line with the BIM4FM principle – ‘as much as necessary, as little as possible’ – it is determined which data is actually required for which specific application: For refurbishment projects carried out whilst the facility is in operation, precise geometries of the building services systems and the structural framework are essential to identify clashes during the planning stage and avoid costly surprises on site. For space and cleaning management, a simplified room structure with room master data, floor area details and component information on surfaces is often sufficient. For maintenance and upkeep, the focus is on plant identification, location and links to technical data. The as-built model produced by the Scan2BIM process is not a blueprint from a new build – but a reliable, validated representation of the building’s actual condition. It forms the basis for all further planning steps in existing buildings. Construction progress monitoring: Automated target-actual comparison with colour-coded visualisation In hospital construction, seamless monitoring of construction progress is particularly critical, as any delay has a

From data chaos to data-driven facility management: Using BIM4FM effectively

From data chaos to data-driven facility management: Using BIM4FM effectively by Lukas Kloppenburg & Arne Müller | May, 20th 2026 back to the overview page Digitalisation in building management is often associated with large volumes of data, extensive BIM models and a high maintenance burden. This is precisely where BIM4FM comes in, offering an alternative approach: it is not the maximum level of data detail that matters, but rather the question of which information, in which context, is crucial for efficient building management. The white paper makes it clear that, in digital building management, a highly detailed as-built model is not necessary for every use case. Rather, what matters is the specific definition of the required data set, as well as structured data collection and availability within facility management. Digitalisation in building management: aspiration and reality Facility management faces a fundamental conflict of objectives. On the one hand, a consistent and up-to-date database covering the entire life cycle of a building is required. On the other hand, creating and maintaining detailed models involves considerable effort, which is why many operators are reluctant to embrace digital building management. This challenge is particularly evident in existing building stock. Information is often fragmented, out of date or difficult to access. Even in new builds, a structured and FM-compatible data foundation is often not defined upon completion, although this is crucial for efficient operation. Added to this is a widespread assumption that digitalisation in building operations is costly and time-consuming. The white paper contrasts this with the real added value: legally compliant documentation, more efficient FM processes, better data availability and long-term cost-saving potential. BIM as a structured foundation for FM Building Information Modelling is increasingly seen as a key solution in building operations. In practice, however, it is clear that, in the context of facility management, a BIM model primarily serves as a structured basis for geometric and alphanumeric data, rather than necessarily as a complete digital representation of the building. For many BIM4FM use cases, room structures, room master data and basic identification data in the model are sufficient. It is particularly important that this data can be clearly linked to further information, such as the CAFM system or a point cloud. The white paper emphasises a clear principle: as much as necessary, as little as possible. What matters is not the maximum level of modelling detail, but the concrete benefit for building operations. A lower level of geometric detail may be sufficient in many cases if the relevant operational data is provided elsewhere in a meaningful way and linked to the model. This approach is particularly important for operators because it reduces the data set to the essentials. Instead of overloading a model with irrelevant information, it is specifically determined which information is actually required for which processes. This ensures structured data maintenance and consistent data availability throughout the entire lifecycle. Point clouds as a digital layer of reality Point clouds play a particularly important role in existing building surveys. They are used to spatially capture the actual condition of a building and to derive geometric and alphanumeric survey data from this. This includes, for example, actual dimensions, the condition of the building, and further information on existing building components and surfaces. A particular advantage is that point cloud data can supplement BIM models and, in some cases, even partially replace them. They create a visual and realistic layer that helps to locate objects more effectively and present building conditions in a comprehensible manner. So-called points of interest allow rooms, building components or technical installations to be precisely located and linked to specific assets in the CAFM system. 360° panoramic images also help to clearly depict the actual building situation and support maintenance or repair processes, for example. This makes the point cloud more than just a surveying tool. It becomes a digital, augmented reality layer that provides orientation, reduces modelling effort and, in conjunction with the BIM model and the CAFM system, generates real added value. CAFM as an operational database Ultimately, all operationally relevant information is consolidated within the CAFM system. Here, geometric and component-related data from the BIM model is linked to dynamic operational information and maintained. The data from the BIM model includes, for example, room names, room numbers, usage types and component IDs. In addition, the CAFM system manages data on maintenance cycles, manufacturer specifications, inspection and operational documentation, as well as other operator-specific information. The particular advantage lies in the fact that this data does not need to be maintained twice in the CAFM system, but can be uniquely assigned to BIM model elements. The link to the BIM model creates a structured and redundancy-free database that is available at all times and supports both operational processes and subsequent analyses. The white paper thus demonstrates that CAFM is not merely a management system, but the central hub for the use and maintenance of essential FM data. Whilst the BIM model provides the static spatial structure, the integrated CAFM system creates a consistent and up-to-date extended FM database. Practical examples in the workplace The greatest strength of the use-case-oriented approach is evident in the specific FM processes. In cleaning management, the BIM model is primarily used to map room layouts and identify areas for tendering or contract planning. Cleaning requirements for building component surfaces or window heights for the use of cleaning equipment can also be derived from it. For this use case, only a low level of geometric detail is usually required. A representation of the room-enclosing components at a lower level of detail is sufficient in most cases. In the context of more complex façade cleaning or the determination of glass surfaces, a detailed geometric representation of these components may be useful. The use of a BIM model linked to additional cleaning-related information simplifies the process of quantity take-offs, facilitates the transparent creation of specifications, and contributes to visually comprehensible cleaning planning. Furthermore, it supports the efficient management of external service providers. In

Digitalisation in the construction industry with BIM: Strategies for sustainable competitiveness

Digitalisation in the construction industry with BIM: Strategies for sustainable competitiveness von Alexandra Nestorowicz & Zoë Gebicke | April 22nd, 2026 Back to the overview The construction and property sector is undergoing a profound transformation. Digital technologies are transforming not only individual processes, but the entire way in which projects are planned, built and operated. At the heart of this transformation lies Building Information Modelling (BIM). BIM is a collaborative working methodology for the model-based, lifecycle-spanning analysis of construction projects and is therefore a key driver of efficiency, transparency and competitiveness in an increasingly complex and interconnected industry. Particularly in times of rising construction costs, growing regulatory requirements and an increasing shortage of skilled workers, it is becoming clear that traditional working methods are reaching their limits. Companies face the challenge of delivering projects faster, more sustainably and, at the same time, more cost-effectively. This is precisely where BIM demonstrates its strategic relevance. Building Information Modelling (BIM) refers to a digital, model-based method for the integrated design, construction and management of buildings. All relevant information is consolidated into digital BIM models, which serve as central data sources for all project stakeholders. BIM thus replaces traditional 2D design approaches and lays the foundation for transparent, collaborative and data-driven processes throughout a building’s entire lifecycle. Despite its enormous potential, there is currently still a significant implementation gap in practice. Studies show that whilst the industry recognises the need, it often fails to implement it. This is precisely where BIM consultancy comes in: it supports companies in shaping digital transformation in a structured and sustainable manner, placing people at the centre as the decisive factor for success and as decision-makers. A key metric for evaluating investments is the return on investment (ROI). It serves as a benchmark for the economic assessment of projects in both strategic and operational management. Studies show, however, that the uncertainty surrounding the assessment of ROI is one of the biggest obstacles to investment decisions. If projected costs or expected profits cannot be reliably determined, the likelihood of an investment being implemented decreases. This uncertainty may contribute to the declining number of completed factory buildings. A key reason for this situation is that, to date, there has been no holistic approach to cost estimation across the entire life cycle of a factory. A lack of transparency regarding investment and operating costs can lead to a distorted assessment of economic viability. The aim of the approach presented here is therefore to develop a model-based cost estimation method that systematically integrates investment and operating costs and provides a sound basis for investment decisions. Understanding BIM: Market demand, definition and current figures To make strategic use of BIM, it is first necessary to understand why the method has become indispensable today. The construction industry is under immense pressure to improve efficiency: rising costs, a shortage of skilled workers, more complex projects and increasing sustainability requirements are driving the need for innovation. Added to this is the growing pressure from clients, who are increasingly demanding digital working methods, transparency and traceable data models. Current figures clearly underscore this trend: According to a PwC study (2025), 82% of construction companies lack the necessary expertise to fully exploit the potential of digitalisation. According to Bitkom Research (2025), 56% of companies recognise the significant potential of BIM for the construction sector. Only 17% of the experts surveyed state that digital solutions are already actively required in tenders. These figures reveal a clear discrepancy. There is an awareness of the benefits, but structural implementation is lagging behind. BIM is not merely an IT issue, but a holistic approach to process optimisation. Centralised data storage makes information available at all times, thereby reducing planning errors, minimising interface losses and enabling more informed decisions to be made. Companies that integrate BIM at an early stage secure not only efficiency gains, but also a clear competitive advantage. Challenges in BIM implementation Despite the obvious benefits, implementing BIM is a complex process for many companies. Ongoing project work and day-to-day operations make it difficult to introduce far-reaching changes. Often, it is not a lack of understanding of the benefits that is the issue, but rather a lack of capacity to implement them consistently in day-to-day operations. The biggest challenges can be divided into three key areas: Organisational silosMany companies continue to operate in separate departments and linear process chains. BIM, however, requires an integrated approach in which design, construction and operation are interconnected. This shift entails a profound cultural and structural transformation that must take place across all levels of the organisation to foster sustainable success. Skills gapsA major bottleneck is the shortage of qualified specialists. A lack of knowledge regarding BIM methods, software solutions and data standards means that existing potential cannot be fully exploited. Companies must therefore not only be prepared to adopt a new method, but also train their staff accordingly. Technological complexitySelecting suitable software solutions, interfaces and data standards poses major challenges for many companies. Without a clear strategy, inefficient siloed solutions emerge instead of end-to-end digital processes. Added to this is an often underestimated factor: the organisation’s willingness to change. Digitalisation is not just about technology, but above all about changing working methods, responsibilities and ways of thinking. Without targeted change management, many BIM initiatives remain stuck at the pilot stage. Our consulting approach: Structured BIM implementation The BIM-Consulting service addresses precisely these challenges. The aim is to help companies align themselves with BIM not only technically, but also strategically and organisationally. The consultancy approach is modular in structure and can be scaled to suit individual needs, meaning it can be adapted to different companies accordingly. Status Quo AnalysisThe process begins with a comprehensive review of existing processes, system landscapes, digital maturity levels and resources. The aim is to establish clear transparency and awareness of strengths, weaknesses and potential. Strategy and Concept DevelopmentBased on the analysis, individual BIM strategies are developed in collaboration with the company. This includes defining BIM objectives, prioritising BIM use cases

Model-based cost estimation for factories: A holistic approach to planning, construction and operation

Model-based cost estimation for factories: A holistic approach to planning, construction and operation by Franziska Wagner, Dr. Lisa Lenz, Alexandra Nestorowicz & Marcel Potthoff | 12.März 2026 back to the overview page Industrial production is under increasing economic pressure. Geopolitical uncertainties and challenges in international trade are dampening growth in the German economy and leading to continued volatility in industrial production and order intake. One example of this is the 3.6% decline in manufacturing output in June 2025 compared with the same month last year. In such an economic environment, secure investment decisions are becoming increasingly important for companies. A key indicator for evaluating investments is return on investment (ROI). It serves as a benchmark for the economic evaluation of projects in both strategic and operational management. However, studies show that the uncertain assessment of ROI is one of the biggest obstacles to investment decisions. If projected costs or expected profits cannot be reliably determined, the likelihood of an investment being implemented decreases. This uncertainty may contribute to the decline in the number of completed factory buildings. A major reason for this situation is that there is currently no holistic approach to determining costs over the entire life cycle of a factory. A lack of transparency in investment and operating costs can lead to a distorted assessment of economic efficiency. The aim of the approach presented here is therefore to develop a model-based cost calculation that systematically integrates investment and operating costs and creates a sound basis for investment decisions. Existing approaches to the economic evaluation of factories Various approaches already exist in factory planning for the economic evaluation of planning alternatives. One example is the VDI 5200-4 guideline, which describes a methodical approach to extended profitability analysis. The aim of this method is to evaluate planning variants holistically by taking into account not only monetary cash flows but also non-monetary target variables. These include, for example, adaptability or employee orientation, which can be systematically converted into monetary variables. This supports a comparison of variants in factory planning. Other approaches deal with the evaluation of life cycle costs and sustainability aspects. For example, a holistic life cycle analysis aims to record investment and operating costs together with environmental impacts such as energy consumption or emissions. Here, too, the focus is on decision support in the selection of planning variants. Another approach concentrates on forecasting the life cycle costs of manufacturing technologies in early planning phases. The focus here is particularly on the energy and maintenance costs of production facilities. The aim is to be able to compare technological alternatives at an early stage, even if it is not yet possible to determine the exact costs at this point in time. Other research projects are attempting to integrate life cycle costs into digital factory models. This makes the cost implications of decisions in production planning visible at an early stage. In some cases, databases or IT architectures are being developed for this purpose, linking cost models with simulations in order to forecast cost indicators over time. What all these approaches have in common is that they are designed to support investment decisions. However, there is currently no standardised cost structure that enables property-specific and model-based cost calculation. Costs are often viewed as aggregate figures or structured on a project-specific basis, which limits their comparability. Standardised cost calculation in construction In the construction industry, DIN 276 has been an established standard for structured cost calculation for many decades. The standard defines terms and principles of cost planning as well as the structuring of costs into different cost groups. This enables a uniform classification of construction costs and improves the comparability of projects. At the first level, DIN 276 comprises eight cost groups, which are identified by three-digit numbers from 100 to 800. These include costs for land, building structures, technical installations and outdoor facilities. Ancillary construction costs and financing costs are also taken into account. The cost breakdown is divided into further levels in greater detail. At the second and third levels, the individual cost groups are further specified so that costs can be precisely allocated to individual components of a building. This allows a building to be viewed as a complex system of different components whose costs can be systematically recorded and controlled. While DIN 276 focuses primarily on the investment costs of a building, operating costs must also be taken into account for a holistic assessment. For this purpose, the VDI 2067-1 guideline offers a structured procedure for determining the operating costs of technical and structural facilities. The guideline distinguishes between capital-related, demand-related, operating-related and other costs. It also provides tables with average useful lives and expenditure values for maintenance, inspection and repair. These values make it possible to realistically forecast operating costs at an early stage of planning and to include them in economic assessments. Model-based cost calculation with BIM Digital methods are playing an increasingly important role in construction project management. One key method is Building Information Modelling (BIM), in which a building is represented as a digital model containing comprehensive information about its entire life cycle. In the context of cost planning, this approach is referred to as model-based cost estimation or 5D planning. Information from the digital building model is linked directly to cost data. Quantity and measurement data such as areas, volumes or lengths can be automatically derived from the model and assigned to the corresponding cost groups. An important component of this method is the use of component libraries or manufacturer-specific databases. These contain pre-stored cost parameters that can be automatically incorporated into the calculation. Additional attributes such as material properties or technical requirements enable more precise adjustment of the cost values. A major advantage of model-based cost determination is that cost information can be continuously updated as planning progresses. Changes in the digital model have an immediate effect on cost planning. This allows target/actual comparisons to be made and potential deviations to be identified at an early stage. In addition, model-based cost

Digital planning, construction and operation at IPS: Why clear BIM standards are the key to success

Digital planning, construction and operation with IPS: Why clear BIM standards are the key to success by Prof. Dr. Lisa Lenz | February 20th, 2026 Back to the overview page Immobilien- und Projektmanagementgesellschaft Sachsen-Anhalt mbH (IPS) is one of the most versatile and innovative construction companies in Germany. The company carries out a wide range of different construction projects, from the development of complex existing properties and comprehensive renovations to modern new buildings. This diversity inevitably leads to very heterogeneous requirements. Different project sizes, changing stakeholders and individual operator requirements mean that adaptability is essential in every project. To make this complexity manageable while increasing quality, transparency and efficiency, IPS relies on Building Information Modelling (BIM). The aim is to use BIM strategically, consciously and practically in order to be able to transfer the BIM methodology to various areas of application. Together with Building Information Management GLW GmbH (BIM GLW), IPS has developed an approach that defines clear standards, structures processes and, at the same time, provides the necessary flexibility to ensure that BIM and a holistic approach to digitalisation create real added value in everyday project work, from project development and planning to construction and operation. AIA reimagined: Why the success of projects always begins with clear decisions and requirements Anyone who wants to successfully implement a BIM project needs a stable starting point, which is provided by IPS’s client information requirements. These form the methodological basis for all digital activities within the company. The AIA specify which data, models and structures IPS requires in which project phase, which attributes are relevant, which formats must be delivered and how information flows during the course of a project. But what makes the IPS AIA special is not only its clarity, but also its practical relevance. During development, care was taken to request only information that is truly relevant to planning, construction and operation. Excessive or contradictory requirements, which are commonplace in many AIA documents in the industry, were consistently avoided. This creates a framework that challenges project participants, but at the same time forms a stable basis for requirements, quality assurance and data analysis. This focus on real processes and early decisions by the IPS ensures that all project participants can find their way around more quickly, tasks can be distributed more efficiently and a common technical language can be developed. The AIA is therefore much more than a document: it is the basis for a structured and sustainable understanding of BIM throughout the entire project environment. From paper to practice: How specifications in the BEP become a functioning and successful project While the AIA defines the strategic perspective, the BIM execution plan (BEP) incorporates these requirements into the day-to-day operations of projects. The BEP describes exactly how BIM models are structured, what roles and responsibilities exist, how information exchange is organised, and what quality checks and data evaluation processes take place and when. The balanced relationship between uniform standards and project-related, individual adaptability is particularly valuable here. IPS relies on a modular BAP system that creates a recurring structure across all projects while leaving enough leeway to take into account the special features of individual construction measures. This results in a BIM process that creates clarity and commitment for all project participants. This leads to fewer coordination problems in the planning and implementation phase, comprehensible planning statuses, transparent decision points and more efficient processes. Structure instead of chaos: Why MEM ensures the quality of models in the long term The Model Element Matrix (MEM) is one of the most effective tools in IPS’s digital project management. It defines how components are structured, what information (attributes) they contain and what level of detail is required in the various project phases. The MEM not only ensures that BIM models are technically sound, but also creates consistency, which is a significant advantage in practice. Planning offices know exactly how objects must be named, classified and attributed. Construction companies receive reliable and comparable information and can create the basis for a robust data structure for the subsequent operational phase. Reusability is particularly valuable: BIM models created in accordance with MEM can be quickly checked, efficiently evaluated and easily transferred to other systems. For IPS, this means a significant increase in data quality and, in the long term, a considerable reduction in redundant work processes, right through to operations. Construction project management primarily uses data-driven AI methods that analyse large amounts of data and derive forecasts or recommendations for action.A particular strength of AI lies in its close integration with existing construction project management processes. AI methods can be used in a targeted manner along the functional distinction between administrative and decision-related processes. While administrative processes benefit in particular from automation techniques such as NLP or computer vision, learning-based methods such as machine learning or deep learning support complex decision-making processes. In combination with BIM, integrated digital project environments are created in which data is used consistently and evaluated continuously. This symbiosis not only enables more efficient processes, but also a new level of transparency and traceability. Project participants gain a better overview of the project status, risks and dependencies, which significantly improves collaboration and control. From BIM model to long-term benefits: How IPS takes a holistic view of the entire life cycle with FM & CAFM concepts Many BIM projects have shortcomings in that, although the methodology works excellently in the planning and, where applicable, construction phases, the data obtained is not transferred cleanly to the operational phase at the end. In this context, IPS pursues a holistic approach in order to achieve maximum efficiency gains, especially in the longest and most cost-intensive phase of the life cycle, namely operation. An operator and CAFM concept has therefore been developed that precisely describes what information is required for subsequent operation of the associated processes, how this information must be structured and how it is transferred to a CAFM system (computer-aided facility management system). A CAFM system is a software solution that digitally maps

Artificial intelligence in construction project management: How data-driven decisions promote efficiency

Künstliche Intelligenz im Bauprojektmanagement: Wie datenbasierte Entscheidungen die Effizienz fördern by Prof. Dr. Lisa Lenz | January 13th, 2026 back to the overview page Construction projects today face a wide range of challenges. Increasing project complexity, high cost and deadline pressure, and growing regulatory requirements characterise everyday life in construction project management. Traditional methods are increasingly reaching their limits. At the same time, digitalisation in the construction industry is advancing steadily, opening up new opportunities for managing projects more transparently and with less risk. Artificial intelligence (AI) plays a key role in this transformation process. It enables the automation of administrative tasks, the intelligent use of large amounts of data, and well-founded support for decision-making processes throughout the entire building life cycle. In conjunction with Building Information Modelling (BIM) in particular, a new quality of project management is emerging that is changing construction project management forever. AI as a driver of efficient construction project processes Effective construction project management is the basis for construction projects that meet deadlines, budgets and quality standards. Construction project management is typically divided into different project phases based on the life cycle of a building: project initiation, planning, execution, monitoring or control, and project completion. Numerous operational, coordination and administrative processes are carried out within these phases. These include defining the construction targets, scheduling and resource planning, budgeting, quality control and risk management. In practice, these processes are often characterised by a high level of administrative effort. Documentation, reporting and coordination in particular require considerable time resources. Studies show that project managers spend a large part of their working time on administrative tasks that do not directly contribute to value creation. This situation can also be applied to construction project management. This is where the use of AI comes in. AI-based systems enable the automation of repetitive tasks, the structured processing of information and intelligent process support. The interfaces between project phases are particularly critical here, as this is where information loss, redundancies and inefficient communication channels can occur. The use of AI can reduce these risks by ensuring that information is processed consistently and made available transparently to all project participants. The result is a significant reduction in the workload for project management and greater process efficiency. Data-driven decision-making in construction In modern construction practice, the availability and quality of data is a key success factor. Construction projects generate a wealth of digital information from various sources, including BIM models, project management software, sensor data, drone images and construction logs. Decisions made during the course of a project, e.g. regarding scheduling, cost control or the selection of construction methods, are increasingly based on this data. Traditionally, decision-making processes in the construction industry have relied heavily on empirical knowledge, manual evaluations and subjective assessments. The use of AI is fundamentally changing this basis. Algorithms can analyse historical project data, recognise patterns and derive well-founded recommendations for decisions. This makes decision-making processes more objective and transparent, while also documenting them as standard. One key area of application is predictive analytics. By comparing current project data with historical data from similar projects, potential schedule deviations or cost overruns can be predicted at an early stage. Risks become visible before they occur, enabling proactive action to be taken. In addition, natural language processing (NLP) enables the automated analysis of unstructured text data such as construction diaries, defect reports or reports. This information, which was previously difficult to use, is structured and made accessible for decision-making processes. Construction project management is thus evolving from a reactive to a data-driven, forward-looking control approach. Symbiosis of AI and construction project management Digitalisation in the construction industry is advancing steadily, and AI is playing an increasingly central role in this process. AI encompasses various technological approaches, including rule-based systems, machine learning, deep learning and hybrid methods. Construction project management primarily uses data-driven AI methods that analyse large amounts of data and use it to derive forecasts or recommendations for action. One particular strength of AI lies in its close integration with existing construction project management processes. AI methods can be deployed in a targeted manner based on the functional distinction between administrative and decision-making processes. While administrative processes benefit in particular from automation technologies such as NLP or computer vision, learning-based methods such as machine learning or deep learning support complex decision-making processes. In combination with BIM, integrated digital project environments are created in which data is used consistently and evaluated continuously. This symbiosis not only enables more efficient processes, but also a new level of transparency and traceability. Project participants gain a better overview of the project status, risks and dependencies, which significantly improves collaboration and control. Multimodal AI architectures for integrated data usage Construction projects are characterised by a multitude of heterogeneous data sources. Service specifications, planning documents, BIM models, expert reports and protocols are often available in different formats and systems. This fragmentation leads to media breaks, inconsistencies and increased coordination efforts. Especially in early planning phases, unrecognised errors can have a significant impact on costs and deadlines. Multimodal AI architectures offer a promising solution here. They combine various AI technologies such as natural language processing, image recognition, computer vision and structured model analyses to evaluate construction project data holistically. The aim is to bring together information from different sources, structure it and make it usable for subsequent processes. A key element is the automated analysis of unstructured text data. With the help of NLP, construction diaries, property descriptions and emails can be evaluated and transferred to structured knowledge databases. This systematically harnesses the experience gained from previous projects and enables cross-project learning. In addition, planning documents can be checked automatically, planning delivery lists can be reconciled, and the completeness of tender documents or building applications can be ensured. BIM models can also be checked for their level of detail and compliance with standards and guidelines. The early identification of contradictions or missing information reduces risks and significantly speeds up decision-making processes. AI in operation: automated data generation

Efficient construction project data analysis with AI: How BIM and laser scanning are transforming automation in civil engineering

Efficient construction project data analysis with AI: How BIM and laser scanning are transforming automation in civil engineering by Prof. Dr. Lisa Lenz | Dezember 9th, 2025 back to the overview page For years, the construction industry has been faced with the challenge of efficiently utilizing enormous amounts of data. Although information from planning, construction, and operation is generally available, it is often unstructured, redundant, or isolated. This leads to interface problems, delays, and suboptimal decisions. With increasing digitalization, inventory digitization through laser scanning, and the spread of BIM (Building Information Modeling), new ways of structuring data and processing it intelligently are emerging.Artificial intelligence (AI) in particular opens up new potential: automated analyses, predictive analytics, machine learning, and multimodal models improve decisions, increase transparency, and optimize processes throughout the entire life cycle of a building. Based on current research and practical content, the following blog post shows how a high-quality database is created, how AI and BIM interact, and how interactive, transparent AI models are changing civil engineering in the long term. Data quality as a foundation: How good data determines project success Data has long been a valuable resource in digital construction—provided it is of high quality. However, in civil engineering in particular, data is often available in different formats: text documents, images, BIM models, sensor data, scans, or manual entries. This heterogeneity makes it difficult to use data consistently throughout the entire life cycle—from inventory and planning to operation. Data quality describes the ability of information to enable reliable decisions. If data is incomplete, out of date, or inconsistent, this has a direct impact on subsequent processes. According to the “garbage in, garbage out” principle, even the most modern AI tools can only work as well as their input data.An example: Daily construction reports contain important data on personnel, weather, equipment, and incidents. However, depending on the perspective of the client or contractor, requirements for comprehensibility and processability differ. If data is changed manually or recorded incompletely, this can significantly impair subsequent processes. Sustainable digital transformation in the construction industry therefore requires consistent data quality management. Standardization, clear responsibilities, automated checks, and clean data structures are crucial for building accurate and reliable AI analyses later on. Digital data generation: From manual entries to automated scan and sensor data Data is generated in two ways in the construction industry: manually or automatically.Manual entries—for example, in Excel, construction log tools, or text documents—are flexible but prone to errors. Automatic data generation using sensors, laser scanners, cameras, or external sources, on the other hand, offers scalability, consistency, and efficiency. The modern construction process is increasingly benefiting from hybrid models: Standardized information such as weather or date can be inserted automatically. Project-specific data such as equipment use or special events are added manually. Interfaces to machine data and delivery notes reduce redundancies. The increasing digitization of inventories through laser scanning and 360° imaging is particularly relevant. Point clouds, image data, and sensor values form a highly precise basis for subsequent BIM models, condition analyses, or maintenance strategies.The higher the degree of automation, the more consistent and usable the data becomes—a significant advantage for AI-supported evaluations. For companies, this means that a structured data management concept is essential to ensure data quality and reusability. BIM data management: The basis for intelligent, transparent processes BIM has long been more than just a 3D model. It forms the central data platform in the construction industry and links geometric information with alphanumeric data on materials, CO₂ emissions, cost indicators, conditions, and much more. Clear information requirements (level of information need) determine which data is relevant for which use cases, such as: Optimization of schedule and cost control Sustainability assessments Automated condition checks Variant comparisons Maintenance strategies A consistent, complete database is indispensable, especially in civil engineering, where safety-related decisions are made. BIM enables a standardized structure that makes AI applications meaningful in the first place. The combination of BIM with automated data sources such as laser scans and sensor technology is particularly valuable. Integrating this information creates a living, constantly updated model, comparable to a digital twin, which provides the basis for analyses, simulations, and decision support. Open, database-supported BIM data management, ideally based on IFC standards, is therefore a central component for the reliable and scalable use of AI in civil engineering. AI data analysis: From predictive analytics to machine learning AI processes can be used efficiently with a structured database. Modern AI analysis concepts make it possible to process complex data sources and convert them into valuable knowledge. The most important AI methods include: Text mining and semantic analysis: automated evaluation of reports, protocols, and documentation Geospatial analysis: combination of GPS, sensor, and environmental data Multimedia analysis: transcription, pattern recognition in audio and video files Machine learning: systems learn from data and continuously improve themselves Predictive analytics: forecasts on risks, construction time developments, or deviationsData mining: identifying patterns and correlations from large data sets An example: construction site reports can be automatically read and linked to weather or project data. AI recognizes deviations, potential additional claims, or risks before they become critical. AI also makes inventory digitization much more efficient: point clouds are automatically classified, damage is detected, and components are semantically assigned. This results in highly accurate models that can be integrated into BIM and FM systems. The result: less manual evaluation work, faster insights, and higher-quality decisions. AI in practice: collaboration, white-box models, and a multimodal future Automation alone is not enough—user acceptance is crucial. Many traditional AI systems are “black boxes”: they deliver results whose origins are not transparent to users. This reduces trust and inhibits their use in civil engineering. Future-oriented AI projects therefore focus on: Interactive collaboration between civil engineering, data science, and project participants Prompt engineering to integrate expert knowledge directly into AI control Explainable AI (XAI) that delivers comprehensible results Adaptive models that evolve through user inputIntuitive visualization, e.g., through dashboards The next step is particularly exciting: multimodal AI models.These systems can process different data formats

Diversity and representation in the construction industry: Why variety builds the future

Diversity and representation in the construction industry: Why variety builds the future by Alexandra Nestorowicz | November 4th, 2025 back to the overview As head of the BIM consulting division at BIM GLW, I see every day how strongly the construction and tech industries are still shaped by traditional structures. As a civil engineer in a male-dominated environment, I am often confronted with a remarkable homogeneity, not only in terms of gender, but also in terms of career paths and dominant mindsets. But it is precisely this uniformity that harbors risks: it inhibits innovation, exacerbates the shortage of skilled workers, and can impair attractiveness as an employer. Diversity is therefore not a nice-to-have, but a decisive success factor for the future of our industry. Why diversity and representation are essential in the construction and tech sectors The situation is similar in many areas of construction and technology. Men dominate management and technical positions, while women, career changers, and people from different cultural and professional backgrounds are still significantly underrepresented. This is deeply rooted in the industry’s tradition; many managers and specialist planners come from similar educational backgrounds, have comparable professional biographies, and share a technology-oriented, often hierarchical understanding of work. But it is precisely this uniformity that can become a challenge. When planning and project teams consist predominantly of people with similar experiences and ways of thinking, so-called blind spots arise. These aspects, which are overlooked or not perceived by the majority due to their familiar perspective or out of habit, are therefore underrepresented in the planning of construction projects, in software development, or in decision-making processes, even though they are important. Different perspectives are missing, which can lead to projects being less user-centered, less creative, or less adaptable. Diversity, on the other hand, promotes a change of perspective. Teams with different cultural, social, and professional backgrounds not only contribute new ideas but also challenge existing processes, which is a crucial factor for innovation. Particularly in the context of the digitalization of the construction industry, where Building Information Modeling (BIM) is playing an increasingly key role, it is clear that change can only succeed if mindsets are broken down and new perspectives are integrated. Diversity is therefore not purely a social issue, but a driver of innovation. It broadens horizons, enables more open communication, and promotes solutions that better meet the diverse requirements of modern construction projects—from sustainability and user-friendliness to cost-effectiveness. Companies that see diversity as a strategic goal secure a long-term competitive advantage while increasing their resilience in an industry undergoing historic change. The status quo: Where the industry currently stands A glance at the figures makes it clear how great the challenge remains. Women currently account for around 10% of the global construction industry workforce. The situation is even more pronounced in technical and digital roles, such as software development, cloud architecture, and data management. Across Europe, only around 8% of skilled workers in these fields are female. Germany is in a slightly better position in international comparison, but even here women are still severely underrepresented in construction and engineering professions. In large listed companies, around 25% of managers are now female, a progress that is only slowly gaining ground in the construction and tech industries. Particularly striking is the low representation of women in technical management positions or in project management, where decision-making power and creative freedom arise. A glance at the figures makes it clear how great the challenge remains. Women currently account for around 10% of the global construction industry workforce. The situation is even more pronounced in technical and digital roles, such as software development, cloud architecture, and data management. Across Europe, only around 8% of skilled workers in these fields are female. Germany is in a slightly better position in international comparison, but even here women are still severely underrepresented in construction and engineering professions. In large listed companies, around 25% of managers are now female, a progress that is only slowly gaining ground in the construction and tech industries. Particularly striking is the low representation of women in technical management positions or in project management, where decision-making power and creative freedom arise. How BIM GLW embraces diversity The BIM GLW demonstrates that diversity in the construction and technology industry is not only possible, but can also be a real success factor. With women making up over 50% of its workforce—more than twice the industry average—and a female-dominated management team (three women, two men), the company is sending a clear message. This composition is no coincidence, but rather the result of a conscious corporate strategy. BIM GLW sees diversity as part of its own identity. In an environment that straddles civil engineering, digitalization, and consulting, diversity is the basis for innovation. Different perspectives lead to better results, especially in a field such as building information modeling, where technical know-how, strategic thinking, teamwork, and strong communication skills come together. Experience shows that when teams are interdisciplinary and diverse, digital construction processes are not only implemented more efficiently, but also more practically. Employees with different professional backgrounds, whether in architecture, IT, business, or construction management, bring complementary perspectives to BIM consulting. This results in solutions that are technologically sophisticated and application-oriented at the same time. Diversity also has an internal impact. Employees experience an environment in which individual strengths are valued and personal development is encouraged. Externally, BIM GLW demonstrates that a modern, open company can actively shape the industry and thus serves as a role model for a new generation of construction and tech companies in which equality and innovation go hand in hand. The added value of diversity and why diversity pays off economically The advantages of diverse teams are now empirically proven. According to a study by the consulting firm Keevee, companies with diverse management teams outperform their competitors in terms of innovation and profitability by an average of 21%. This correlation is no coincidence, as different perspectives lead to more holistic decisions, better risk management, and greater adaptability in

Successful overall BIM coordination: Quality control as the key to error prevention in practical application

Successful overall BIM coordination: Quality control as the key to error prevention in practical application by Arne Müller | October 15th, 2025 back to the overview page Everyone is talking about BIM and digital planning. But what happens when all the specialist models come together? This is exactly where overall BIM coordination comes in. It is the control center of modern construction planning. However, when many participants and data come together, pitfalls can arise: missing models, overworked coordinators, or chaotic communication. In this article, you will learn about the problems that typically arise in overall BIM coordination and how well-thought-out quality control keeps projects on track. What is Building Information Management (BIM) and what are the problems associated with its application? Building Information Modeling (BIM) is considered a key technology for the future of construction. The method promises more efficient planning, fewer errors, and significantly greater transparency across all project phases. By digitally connecting all parties involved, construction projects are to be realized more quickly, cost-effectively, and sustainably. However, practice paints a different picture: there is often a significant gap between aspiration and reality. While architectural, structural, building services, and other specialist models are to be brought together in a common coordination model, the first difficulties quickly arise. Different software standards, poor data quality, and unclear responsibilities often lead to misunderstandings and duplication of work. Missing or incorrect information, overloaded models, or imprecise interface descriptions are not the exception, but rather a bitter reality in many projects. Especially in complex large-scale projects, this can quickly lead to significant delays and cost increases. To ensure that the digital theory of BIM does not end in planning chaos, clear processes, unambiguous communication channels, and above all, consistent quality assurance are required. Only through regular model checks, coordinated data structures, and transparent project organization can the full potential of BIM be exploited along the entire value chain. What exactly is BIM overall coordination? Overall coordination is at the heart of BIM planning. It ensures that all specialist models are regularly merged, checked, and coordinated. The aim is to create a consistent, transparent, and collision-free overall model that serves as a common basis for all project participants. A key objective is to identify conflicts at an early stage. If, for example, a pipe runs through a beam, this problem can be identified and resolved in the digital model before it leads to costly surprises on the construction site. Equally important is the creation of consistency. All participants work with the same data, which reduces misunderstandings and makes processes clearer. In addition, overall coordination contributes to transparency. Builders and users are given the opportunity to better understand the planning and actively participate in shaping it. Another advantage is the acceleration of decision-making processes. Instead of gut decisions, project participants can rely on verified and validated data. Clear basic principles are needed to achieve these goals. Close cooperation instead of isolated working methods, the early involvement of clients and users, the use of open standards such as IFC or BCF to overcome software limitations, regular model merges, and clearly structured coordination rounds are crucial. Typical problems in practice So much for theory, but what about practice? Many projects show that overall coordination fails at the same points. The most common pitfalls Missing or delayed modelsDeadlines are not met. If specialist models are delivered late or not at all, the entire coordination process comes to a standstill. Decisions are delayed and the next coordination meeting loses its significance. Overloaded or faulty modelsSome models are overly detailed, down to individual screws. This may be technically impressive, but it adds no value and instead overloads the overall model. Conversely, other specialist models lack crucial geometries or attributes that would make it possible to check them in the first place. Software incompatibilityNot all software “speaks” the same language. When exporting to IFC, geometries can be lost, attributes can be missing, or models may not be read correctly. The result: time-consuming rework and coordination problems. Lack of expertiseBIM is not a sure-fire success. Not all project participants are familiar with standards, tools, or processes. This deficit leads to faulty modeling, misunderstandings, and considerable time losses. Unclear responsibilitiesWho maintains the overall model? Who documents conflicts? Who decides on solutions? Without clear roles and responsibilities, tasks end up in no man’s land and remain unresolved. Chaotic communicationConflicts or problems are identified but not systematically documented. Without priorities, deadlines, and clear documentation, issues pile up and urgent problems remain unresolved. Lack of standardsWhen each trade models according to its own rules, inconsistencies arise. Different levels of detail (LOD) make comparisons difficult, and inconsistent component structures lead to misunderstandings. Lack of resourcesCoordination takes time. But in many projects, it is done “on the side” – without sufficient capacity for review, documentation, and communication. The result: coordination falls by the wayside. The consequences: Collisions in the model, unnecessary additional work, frustration in the team – and ultimately rising costs. Quality control as a game changer How can chaos in overall coordination be prevented? The answer lies in consistent and well-thought-out quality control. This not only makes BIM coordination more efficient, but also ensures confidence in the overall model. Technical testing Technical quality assurance is a key component. This includes automated collision checks that reveal conflicts between trades before they lead to costly problems on the construction site. Attribute checks are equally important—for example, for fire protection classes, materials, or component assignments. This ensures that the models are not only geometrically correct, but also contain technically accurate information. The whole process is supplemented by geometry checks that verify the completeness and plausibility of the components. Only when all parameters are correct can the overall model serve as a reliable basis. Organization and processes However, technology alone is not enough. Without clear organizational structures, even the best audit will be ineffective. That is why it is crucial to clearly define responsibilities from the outset: Who conducts the audits, who documents the results, and who ultimately makes the