Researching together and creating knowledge for a growing ecosystem

Künstliche Intelligenz in der Bauwirtschaft: Grundlagen und Anwendungsfälle

Buchveröffentlichung (2024)

This specialist book presents the potential of artificial intelligence (AI) in construction. The increasing amount of data and the use of hardware and software in construction projects enable cross-project analyzes using methods such as machine learning and robotics. The articles provide an overview of AI methods and show how AI is used in construction using concrete use cases from research and practice. This promises efficiency gains, error reduction through automation and new options for decision support. The book is aimed at managers and experts in the construction industry who are striving for sustainable improvements in their company processes using AI.

Bewertung analytischer Planungsmethoden zur Erhöhung der Prognosesicherheit von Rahmenterminplänen bei Bauprojekten

Dissertation (2023)

In construction projects, early forecasts of project durations are crucial, as deviations have a significant impact on deadlines, stakeholder requirements and project processes. However, practice shows challenges in accurate forecasts due to unknown information and systematic planning errors. Research is increasingly focusing on analytical planning methods to reduce these errors. This work examines the use of such methods in early construction project phases, highlights strategies for reducing deviations and compares the forecast accuracy of different analytical approaches. The results provide building owners with guidance for long-term planning and the targeted use of analytical methods in early project phases.

Investigating the Impact of Lean Leadership on Construction Project Success

Part of Engineering Management Journal (2023)


Lean, crucial for optimizing construction project performance, necessitates more than tools; it demands a cultural shift. Lean Leadership emerges as pivotal, integrating Lean principles with leadership behavior for lasting change. Despite limited studies in the construction industry, this research introduces novel components of Lean Leadership. A conceptual framework explores the relationship’s impact on construction project success. An online survey of U.S. lean construction practitioners substantiates a direct, positive association between Lean Leadership and project success, emphasizing the transformative potential of effective leadership in implementing Lean principles in construction projects.

A comparative evaluation of decision trees and expert intuition to predict durations in the predesign phase

European Conference on Computing in Construction and the 40th International CIB W78 Conference (2023)

This study assesses the prediction accuracy of CatBoost and expert intuition in estimating the duration of public construction projects during the predesign phase. Using a dataset of 367 projects from New York City, the comparison involves Mean Absolute Error (MAE) and absolute preference as performance indicators. Expert intuition exhibits high outliers, whereas CatBoost provides more consistent predictions. In uncertain situations, especially, CatBoost demonstrates practical advantages over human expert estimations, highlighting its potential utility in enhancing accuracy and reliability in construction project scheduling

Combining BIM and lean methods for scheduling for verifying and communicating demolition processes in an early project stage

European Conference on Computing in Construction and the 40th International CIB W78 Conference (2023)

Demolition projects, fraught with challenges like safety regulations and diverse stakeholders, often involve complex data in various formats. This paper advocates a solution combining BIM, project data, and Lean scheduling for early project visualization and simulation, requiring minimal client effort. Illustrated through an industrial demolition project, the method enhances communication and reduces complexity. The digital model, integrating all relevant data, fosters stakeholder understanding, enabling efficient communication, quicker decision-making, and evaluation of demolition schedules. This approach proves effective when a BIM model and Lean schedule are available, streamlining processes for improved project outcomes.

Challenges in collecting and managing data for AI application in small and medium-sized construction enterprises

European Conference on Computing in Construction and the 40th International CIB W78 Conference (2023)

Demolition projects encounter challenges adopting smart tools like BIM, Big Data, and AI in construction. Despite their prevalence, integration into daily operations is limited. A case study with a German construction SME highlights data collection challenges. Though digital and data-driven, insufficient data availability and quality hinder effective AI implementation. The paper suggests implications for SMEs to harness AI benefits in the future, emphasizing the need for enhanced data practices in construction projects for successful integration of advanced technologies.

Process Analysis with an Automatic Mapping of Performance Factors Using Natural Language Processing

31st Annual Conference of the International Group for Lean Construction (2023)

In lean construction, efficient methods are crucial for utilizing data collected during process analysis. This paper aims to automate the mapping of historical performance factors to new project specifications using Natural Language Processing (NLP). The NLP model compares process descriptions, filtering the relevant performance factors and calculating durations. This supports process analysis, aiding in generating a validated construction schedule. The case study demonstrates promising results in mapping quality and prediction accuracy. The developed concept allows last planners to validate duration estimates in lean construction process planning, promoting project stability. A more detailed process description could further enhance prediction results.

Utilising Design Thinking to Refine Customer Requirements – a Case Study Using the Concrete Supply Chain as an Example

31st Annual Conference of the International Group for Lean Construction (2023)

The German concrete supply chain faces challenges due to its high participant count, resulting in complex interface management. Previous attempts at establishing a digital information chain emphasized technology over user needs, leading to limited success. This paper presents a human-centered research methodology using Design Thinking to actively involve users, aiming to reduce waste and unnecessary activities in the concrete supply chain. Focusing on the ultimate customer, the approach refines customer requirements and offers a model for Design Thinking application in addressing industry challenges.

International assessment of artificial intelligence applications in the AEC sector

Part of ECPPM 2022 - eWork and eBusiness in Architecture, Engineering and Construction 2022 (2023)

The construction industry grapples with stagnant productivity and slow digitalization, marked by data heterogeneity and silos. To tackle this, tech companies are leveraging Artificial Intelligence (AI) to analyze complex data and enhance decision-making. This study assesses 236 AI-focused tech companies in Architecture, Engineering, and Construction (AEC), using 16 variables. Their impact, measured by social network followers and revenue, revealed correlations with location, company characteristics, and technology type. The findings indicate untapped innovation potential in unexplored areas, highlighting opportunities for AI adoption and advancement in the construction sector.

Synergies Between Lean Construction and Artificial Intelligence: AI Driven Continuous Improvement Process

30th Annual Conference of the International Group for Lean Construction, 2022 (engl.)​

Both, Lean Construction (LC) techniques and Artificial Intelligence (AI) methods strive for the continuous improvement of production systems in projects and organizations. A combined implementation of both approaches is an ongoing research area. Therefore, the question arises as to whether the added value generated by implementing both approaches jointly is greater than the added value generated by implementing them independently and what is the significance of people in their combined use. This paper explores theoretically the potential of synergies between LC and AI in the AEC sector with exemplary use cases as well as their resulting effects.

An Empirical Evaluation of Predicted Outcomes as Explanations in Human-AI Decision-Making

Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2022 (engl.)

In this work, we empirically examine human-AI decisionmaking in the presence of explanations based on predicted outcomes. This type of explanation provides a human decision-maker with expected consequences for each decision alternative at inference time—where the predicted outcomes are typically measured in a problem-specific unit (eg, profit in US dollars). We conducted a pilot study in the context of peer-to-peer lending to assess the effects of providing predicted outcomes as explanations to lay study participants.

Designing a Human-in-the-Loop System for Object Detection in Floor Plans

Proceedings of the AAAI Conference on Artificial Intelligence, 2022 (engl.)

In recent years, companies in the Architecture, Engineering, and Construction (AEC) industry have started exploring how artificial intelligence (AI) can reduce time-consuming and repetitive tasks. One use case that can benefit from the adoption of AI is the determination of quantities in floor plans. This information is required for several planning and construction steps. Currently, the task requires companies to invest a significant amount of manual effort. Either digital floor plans are not available for existing buildings, or the formats cannot be processed due to lack of standardization. In this paper, we therefore propose a human-in-the-loop approach for the detection and classification of symbols in floor plans.

Predicting the construction duration in the predesign phase with decision trees

In ECPPM 2022-eWork and eBusiness in Architecture, Engineering and Construction 2022

This paper assesses decision trees’ (DTs) effectiveness in predicting construction duration during the predesign phase. Surveying the German AEC industry for expected prediction accuracy, the authors compare five DTs (Random Forest, GBR, XGBoost, LightGBM, CatBoost) with artificial neural networks (ANN) and linear regression models (LRMs) using two residential project data sets. Performance indicators (MAE and MAPE) demonstrate that DTs outperform ANNs and LRMs. Achieving a 26% expected prediction accuracy, data set 1 yields a 13.48% MAPE, while data set 2 approaches it with a 26.45% MAPE. This underscores DTs’ practical potential amid increasing construction data generation, emphasizing the need for further testing their explainability in predicting construction duration.

Artificial Intelligence for the Construction Industry - A Statistical Descriptive Analysis of Drivers and Barriers

Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 1410) (2021)

AI applications in construction are underutilized despite the transformative impact in other industries through digitalization and Industry 4.0. This study aims to identify drivers and barriers for AI implementation in construction and related sectors. Analyzing data from over 100 sources, including 35 selected for relevance, involving feedback from 36,269 companies in Europe and North America, revealed 23 factors. Among them, 15 were drivers (e.g., productivity enhancement), and 21 were barriers (e.g., skilled labor shortage). Economic and technological factors predominantly influenced AI adoption, while social factors featured more in barriers, with fewer political considerations.

Concept of a continuous information chain using artificial intelligence methods using the example of the concrete supply chain

Proceedings of the International BBB-Congress, 2021

Construction projects and their supply chains are complex due to the large number of interfaces. Information chains have many media breaks and information exchange often takes place in analogue form. On the other hand, consistent information chains are required for a stable and low-waste construction process. This publication presents a two-stage implementation model. The model aims to create a continuous information chain with reduced waste of resources. The model is applied as an example to the concrete construction value creation and supply chain. In the first stage, artificial intelligence methods are used and in the second stage, a standardization measure is used.

Digitalization of Lean Learning Simulations: Teaching Lean Principles and Last Planner® System

29th Annual Conference of the International Group for Lean Construction, 2021 (engl.)

This paper analyzes lean simulations on a digital platform that enables real-time interactions between multiple participants. Two simulations were created in a three-stage development process focused on Lean principles and the Last Planner® system. Participant evaluations through questionnaires returned consistently positive results, with technology, fun and design rated highly. The results demonstrate successful interaction via the chosen digital platform and underline the flexibility, easy integration, low costs and high sustainability of digital simulations.

Human-AI Complementarity in Hybrid Intelligence Systems: A Structured Literature Review

28th Pacific Asia Conference on Information Systems , PACIS 2021 (engl.)

Hybrid intelligence is a new concept that emphasizes the complementarity of human intelligence and artificial intelligence (AI). An important prerequisite for collaboration between humans and AI is the interpretability of the decisions made by the AI. As part of a structured literature search, we identify relevant factors that influence collaboration between humans and AI.


Artificial Intelligence for the Construction Industry - a Statistical Descriptive Analysis of Drivers and Barriers

1st International Conference on Disruptive Technologies Tech Ethics and Artificial Intelligence , 2021 (engl.)

In the construction industry, applications of artificial intelligence (AI) are still uncommon. Digitalization, industry 4.0 and the increasing implementation of AI systems already show a rise in efficiency and innovation in many industries. Thus, there are still untapped opportunities for construction.

The aim of this work is to identify drivers and barriers for the implementation of AI in the construction sector and its adjacent industries. Data on pre-vailing factors was collected in a systematic review of more than 100 studies, surveys, and statistics…

BIM Basics: BIM und KI in Wissenschaft & Unternehmenspraxis

buildingSMART Deutschland, 2021 (de.)

The general conditions are changing rapidly due to change and the constant need for renewal for people, society and companies alike. Digitalization touches all areas of life and is at the same time a promise, a curse and ultimately irreversible.

Even if a lot of things seem unclear today and the connections are still being researched, this publication combines the keywords BIM and AI, digital business models, IoT and e-commerce and shows examples from corporate practice.


Specification of a standardized format to define and exchange Model View Definitions with Exchange Requirements and Validation Rules.

Technical Services of buildingSMART International Ltd. , 2021 (engl.)

Technical documentation on GITHUB: new version of the buildingSMART standard mvdXML in version 1.2. This is currently a draft version.

Resource-efficient construction with concrete, with particular attention to the lean construction method

Resource-efficient concrete future strategies for building materials and construction practice (2020)

Construction is a significant economic sector, accounting for 13% of global gross domestic product. Despite this economic relevance, the annual increase in productivity over the last 20 years has remained limited to around one percent. Approaches to solving this dilemma lie in the basic principles of lean thinking, defined as customer value, identification of the value stream, flow-oriented production, demand-oriented manufacturing and the pursuit of perfection. In construction, known as lean construction, this includes production system design, collaborative work planning and integrated project delivery. With regard to resource-efficient construction with concrete, the design and control of production systems will be discussed in particular.

A requirement model for Lean leadership in construction projects

Proceedings 28th Annual Conference of the International Group for Lean Construction (IGLC) , 2020 (engl.)

A growing number of  construction companies are embracing Lean philosophy to enhance customer value and identify waste. A recent survey of Lean Construction Institute members highlighted managerial barriers as primary obstacles. Many companies prioritize Lean methods/tools, often neglecting social factors crucial for cultural change. Site managers, acting as links between value-adding personnel and top management, play a crucial role. A necessary shift from a managerial to a leadership role demands supporting subcontractor collaboration and continuous individual skill improvement. Drawing from comprehensive literature, the authors propose a Lean leadership requirement model for construction managers, offering a foundation for further research and training programs.

Building the future of the construction industry through artificial intelligence and platform thinking

Digitale Welt, 24.01.2020 (engl.)

Data in the construction industry is heterogeneous, organizations do not work closely together, and construction software is highly specialized for individual users and applications. As a result, knowledge from previous construction projects is often not shared, linked, or transferred to subsequent projects. Additionally, the manual work on-site leads to long and unstable design and construction processes. Based on a review of common challenges in this work a new vision for the Architecture, Engineering, and Construction (AEC) Industry is developed...

More security thanks to artificial intelligence

BauPortal 4|2020 (de.)

DT he “Smart Design and Construction” (SDaC) research project [...] aims to optimize safety on construction sites using artificial intelligence. In various use cases, the partially automated creation of risk assessments and the partially automated recognition of dangers on orders are carried out.

You can find out how the application could be designed here: 

The general contractor response to platform ecosystems​

IGLC, 27.07.2019 (engl.)

Platforms enable value-creating interactions between producers and customers by mediating between their users. Supported by digitization, platforms use large datasets and integrated production systems to enhance the customer and producer experience. The platform's business model is expanding in the economy as digitization increases...

How will a future platform disrupt construction industry?