Sustainability is crucial in the construction industry, necessitating the understanding and communication of life cycle assessments (LCA) and the reuse potential of building components for selecting the most sustainable design options. Current LCA software tools provide extensive information, such as certification results exported as Excel tables according to the assessment scheme of the German Sustainable Building Council (DGNB). However, these results can be challenging to interpret without deep LCA knowledge, and the connection to the three-dimensional BIM (Building Information Modelling) model is often lost. This study explores how LCA results and reuse potential can be visualized directly on the IFC (Industry Foundation Classes) model for non-experts. Literature-based analyses identify effective data visualization strategies, followed by the implementation of a prototype in the Unity game engine, incorporating relevant parameters for sustainable construction. The focus is on interaction possibilities with the building model and evaluation results in the 3D environment. Unity enables the comparison of IFC models of different variants, allowing free viewing of the models. A test deconstruction simulation visualizes the deconstruction process of individual parts of the building. In the model viewer, components are colour-coded based on sustainability and assessment uncertainty using transparency values. Parameters such as Global Warming Potential (GWP), circularity, deconstructability, Net Present Value (NPV), and LCA uncertainty are displayed for each variant. The visualization process is partially automated and will be integrated into an ongoing research project.

The reuse of load-bearing reinforced concrete elements represents a promising approach to reducing CO2 emissions and resource consumption in the construction industry. This paper presents the holistic process of modular reuse, ranging from the identification of suitable donor structures through element extraction and characterization to the assembly of new load-bearing structures. Attention is paid to the development of systematic methods for element identification using Scan-to-BIM, as well as to the evaluation of different separation methods in terms of their impact on element quality. Experimental investigations combine non-destructive and semi-destructive testing methods to determine material properties and structural integrity. The results show that, although selective demolition methods require greater effort, they contribute significantly to the preservation of element quality. In addition, a digital element catalogue with a semantic data structure is presented, which enables consistent documentation and further processing of element information. The approaches developed are validated in full-scale demonstration projects and contribute to the establishment of circular construction methods. Modulare Wiederverwendung bestehender Tragstrukturen: Vom Konzept bis zur Demonstration im Originalma ss stabDie Wiederverwendung von tragenden Stahlbetonbauteilen stellt einen vielversprechenden Ansatz zur Reduzierung von CO2-Emissionen und des Ressourcenverbrauchs in der Bauindustrie dar. Dieser Beitrag stellt den ganzheitlichen Prozess der modularen Wiederverwendung vor, der von der Identifizierung geeigneter Spendergeb & auml;ude & uuml;ber die Entnahme und Charakterisierung der Bauteile bis hin zum Aufbau neuer Tragkonstruktionen reicht. Ein Schwerpunkt liegt auf der Entwicklung systematischer Methoden zur Elementidentifizierung mittels Scan-to-BIM sowie auf der Bewertung verschiedener Trennverfahren hinsichtlich ihrer Auswirkungen auf die Elementqualit & auml;t. Experimentelle Untersuchungen kombinieren zerst & ouml;rungsfreie und halbzerst & ouml;rende Pr & uuml;fverfahren zur Bestimmung der Materialeigenschaften und der strukturellen Integrit & auml;t. Die Ergebnisse zeigen, dass selektive Abbruchverfahren zwar einen h & ouml;heren Aufwand erfordern, aber wesentlich zur Erhaltung der Elementqualit & auml;t beitragen. Dar & uuml;ber hinaus wird ein digitaler Elementkatalog mit einer semantischen Datenstruktur vorgestellt, der eine konsistente Dokumentation und Weiterverarbeitung von Elementinformationen erm & ouml;glicht. Die entwickelten Ans & auml;tze werden in Demonstrationsprojekten im Realma ss stab validiert und tragen zur Etablierung zirkul & auml;rer Bauweisen bei.

Numerical models of shield–soil–structure interaction usually assume idealized tunnel boring machine (TBM) operation and rarely account for complex downtime patterns, cascading failures, or actual machine behaviour [1]. This paper does not propose a new soil model. Instead, it presents a data-driven framework for more realistic mechanised tunneling simulations using downtime patterns, advanced sequences, and sensor-based analysis. It is applied to a mixshield tunneling project, for which detailed downtime logs and sensor measurements were available. Downtime codes are categorized and analysed to identify critical error codes and subsystems. Statistically significant failure sequences are extracted using temporal segmentation with sliding windows and a modified PrefixSpan algorithm and are aggregated into directed causality networks at code and subsystem levels to reveal dominant propagation paths.,In addition, multivariate anomaly detection is performed on selected grouting system sensors associated with critical downtime events. Several unsupervised methods including interquartile range fences, kmeans clustering, and isolation forest are evaluated in terms of early warning capability.,The results show that logistics and material supply delays, pipeline extension, and mortarrelated operations dominate projectlevel downtime, while a small number of subsystems act as central drivers of cascading delays. Sensor anomalies correlate with subsequent downtime, though robust early warning remains challenging. The proposed framework supports datadriven maintenance and planning and improves the realism of TBM operational modeling.

Rising global construction demand calls for greater economic efficiency and ecological sustainability. Precast concrete construction (PCC) can reduce on-site errors and waste by using controlled production environments. However, PCC remains limited by traditional, labour-intensive methods ill-suited to automation and individualised elements. Advances in digital fabrication enable customised concrete modules at scale, but realising this potential requires consistent digital representations that integrate design and production. This paper presents an integrated framework leveraging Industry 4.0 concepts to overcome these limitations, specifically employing the Asset Administration Shell (AAS) to implement modular Digital Twins (DTs). Drawing on perspectives from multiple disciplines, this research outlines design and optimisation methods that demonstrate the potential for highly differentiated, precise concrete modules from various digital production processes. Based on a conceptual multi-storey building as a case study, this work explores advances in the design and production of precast concrete modules to highlight the diverse requirements and use cases for DTs in PCC. Three DT case studies are developed, which support design, production, and quality control. These include the simulation-based geometric modularisation of building elements to support early design phases, the monitoring and structuring of production data for analytical insights, and the management of geometric deviations of individual building modules, assessed in relation to their cumulative effect on the overall structural assembly. The results demonstrate the feasibility and effectiveness of integrating the DT concept via the AAS to manage complexity across design and production phases of individualised precast structures, paving the way for more sustainable and efficient construction practices in concrete.

Precast concrete production is increasingly shifting from construction sites to factory environments, yet remains far from fully industrialized or digitally integrated. Processes are still largely craft-based, with automation limited to isolated tasks. A key obstacle is the lack of consistent, machine-readable, and lifecycle-spanning data models. Although Digital Twin (DT) concepts have proven effective in manufacturing, their adoption in precast production is challenged by heterogeneous processes and fragmented information flows. Without adaptation, DTs risk remaining isolated solutions rather than enabling system-wide integration. Within the Priority Program SPP 2187, DT concepts for precast concrete production were developed and refined. This paper consolidates the resulting advances. Based on the Asset Administration Shell (AAS), modular ontology-based data models integrate planning and production and are extended to support lifecycle updates, manage production-induced deviations, and integrate sensor and resource data. The contribution highlights key architectural and methodological challenges in transferring DT concepts to construction. Der Digitale Zwilling f & uuml;r die schnelle und pr & auml;zise Fertigung von BetonmodulenDie Herstellung von Betonfertigteilen verlagert sich zunehmend von Baustellen in Fabrikumgebungen, ist jedoch noch weit davon entfernt, vollst & auml;ndig industrialisiert oder digital integriert zu sein. Die Prozesse sind nach wie vor weitgehend handwerklich gepr & auml;gt, wobei die Automatisierung auf einzelne Aufgaben beschr & auml;nkt bleibt. Ein zentrales Hindernis ist das Fehlen konsistenter, maschinenlesbarer und den gesamten Lebenszyklus umfassender Datenmodelle. Obwohl sich Konzepte des Digitalen Zwillings (DT) in der Fertigungsindustrie als wirksam erwiesen haben, wird ihre Anwendung in der Fertigteilproduktion durch heterogene Prozesse und fragmentierte Informationsfl & uuml;sse erschwert. Ohne Anpassung besteht die Gefahr, dass Digitale Zwillinge isolierte L & ouml;sungen bleiben, anstatt eine systemweite Integration zu erm & ouml;glichen. Im Rahmen des Schwerpunktprogramms SPP 2187 wurden DT-Konzepte f & uuml;r die Betonfertigteilproduktion entwickelt und weiter verfeinert. Dieser Beitrag fasst die daraus resultierenden Fortschritte zusammen. Auf Basis der Verwaltungsschale (Asset Administration Shell, AAS) integrieren modulare, ontologiebasierte Datenmodelle Planung und Produktion und werden erweitert, um Aktualisierungen & uuml;ber den gesamten Lebenszyklus hinweg zu unterst & uuml;tzen, produktionsbedingte Abweichungen zu verwalten sowie Sensor- und Ressourcendaten zu integrieren. Der Beitrag hebt zentrale architektonische und methodische Herausforderungen bei der & Uuml;bertragung von DT-Konzepten auf die Bauindustrie hervor.

Construction management requires integrating cost, schedule, and resource information to balance project objectives. In current practice, these domains are often developed independently, resulting in fragmented data structures, duplicated resource definitions, and limited cross-domain analysis. This paper presents a Knowledge Graph (KG) framework for the semantic integration and consistency checking of construction project information. Building upon an existing ontology stack developed in prior work, scheduling, cost, resource allocation, and building model data are integrated into a KG. To address the scalability limitations of manually establishing cross-domain relationships, the framework introduces a Retrieval-Augmented Generation (RAG) workflow that combines semantic retrieval with Large Language Models (LLMs) to support semi-automatic linking of schedule tasks and cost items. Expert validation remains part of the process to ensure project-specific correctness as demonstrated through a case study. The resulting KG enables SPARQL-based querying and cross-domain consistency checks, reducing manual semantic alignment effort while improving reasoning across domains.

The building industry operates within a complex regulatory landscape defined by national and international standards and legal frameworks. Central among these are the EU Construction Products Regulation (305/2011) and national provisions like the Model Administrative Provisions – Technical Building Regulations (MVV TB), which govern the placement of construction products on the market. Verifying the completeness and compliance of manufacturers’ Declarations of Performance (DoPs) remains a significant challenge across planning, trade, and construction sites. This paper presents a web-based platform that automates the validation of DoPs against regulatory requirements. Users can upload XML-based declarations, which the system checks for conformity and provides detailed error analyses. Structured product data is displayed when compliance is confirmed. The system is built on CEN Workshop Agreement (CWA) 17316:2018, enabling machine-readable processing and integration with national regulations. To define required product information, a methodological approach was developed to extract relevant characteristics from standards such as EN 771-1 and EN 771-2. These requirements were formalized in an optimized XML schema and validated using a hierarchical, XSLT-based rule system. A prototype demonstrated that digital verification reduces effort and increases accuracy, supporting broader digitalization in construction product certification.

In industrial fire detection scenarios characterized by high-ceiling environments, deep learning-based methods exhibit superior efficacy by relying on visual data rather than on smoke density or thermal gradients. However, these models are prone to generating a high rate of false alarms, a problem that is difficult to mitigate due to their inherent black-box nature. To address this limitation, this paper introduces the FireMAS system, which utilizes state-of-the-art Vision-Language Models to incorporate environmental context into model predictions. The approach employs a multi-agent mechanism where independent agents analyze the scene from diverse global and local perspectives and collaboratively validate fire events, thereby reducing false alarms and improving robustness. This system achieves enhanced detection performance by decreasing false positives, resulting in a more reliable detection framework. To the best of our knowledge, FireMAS is the first work to integrate a multi-agent system for incorporating semantic contexts with a deep learning model at the inference stage in the industrial fire detection setting. The integration of our proposed system with a detection model improves the Area Under the Receiver Operating Characteristic Curve (AUROC) by an average of 0.18 points and, in low false alarm regions, by a margin of 11.24 points on industrial datasets. A detailed analysis of the system’s effectiveness confirms that our method can be effectively applied in industrial fire detection use-cases.

The construction industry is increasingly focusing on sustainable practices for reducing CO2 emissions, where the reuse of concrete elements is emerging as a significant area of interest. This study uses simulation modeling to address the multi-criteria distribution problem associated with the logistics of reusing concrete elements from deconstructed buildings in upcoming construction projects. Similar research has been conducted on the simulation of transportation and supply-chain processes in modular construction using Geographic Information Systems (GIS), while the logistic simulation for the reuse of existing non-prefabricated structural concrete elements is largely unexplored. Eventually, the motivation behind this research is to minimize environmental impact by reducing waste and emissions through efficient reuse strategies. We propose a network-based approach that integrates spatial information from GIS to manage building deconstruction and match the demand for reuse elements. In this paper, the requirements regarding system components (e.g., buildings, construction sites, processing facilities, or transportation vehicles), parameters (e.g., coordinates of buildings or facilities, or condition of extracted elements), and process flows (e.g., upgrading of elements based on their condition) are identified from existing literature and formalized in a standardized SysML diagram for the simulation model. While the current model is an initial formalization of reuse logistics with GIS-integrated simulation, it prepares the way for future developments that will address the full complexity of real-world scenarios.

Design decisions made in the early phases of the design process have a significant impact on the eventual performance of the completed building. Currently, computer-assisted methods offer limited support during the crucial stages of creating, assessing and refining design variants. This paper presents an integrated framework combining computer-aided design processes and performance-based evaluation methods to support a non-linear, iterative design process. The framework integrates spatial metrics (e.g., layout and design similarity), structural metrics (e.g., feasible systems and material quantities) and environmental–energy metrics (e.g., heating demand) to provide transparent, quantitative and qualitative feedback for early-stage decision-making. Applying the framework allowed for the incorporation of structural, environmental and spatial assessments early in the process. The design assistance framework is graphically represented using business process model and notation (BPMN), which facilitates communication between process design and implementation. A real-world, mixed-use building scenario illustrates how the individual methods interact to streamline the decision-making process for architects. The framework guides the entire process, from design decisions to structural and performance-specific features, offering inspirational support for architects and practical assistance for structural engineers, sustainability experts and other professionals involved in early-stage building design.

The increasing integration of robotics in the industrial and construction sectors has significantly enhanced productivity and efficiency. However, the rapid adoption of human-robot collaboration (HRC) introduces unique safety challenges, particularly regarding workers' improper use of personal protective equipment (PPE). These issues, if not effectively addressed, can compromise safety and impede the widespread adoption of robotic technologies. This study proposes an automated framework that combines semantic scene graph generation and named entity recognition (NER) to detect unsafe behaviors in real time across diverse HRC scenarios. The method employs encoder-decoder-attention (EDA) architectures and natural language processing techniques to transform unstructured visual data into structured textual descriptions. By integrating domain-specific safety knowledge, the framework enables accurate hazard inference and actionable insights for safety management. For HRC image understanding, the EDA achieved superior results with a convergence loss of 1.089, 99.1% accuracy, and a Bilingual Evaluation Understudy (BLEU)-4 score of 0.8905. Then the detection precisions for entities like human, behavior, unsafe behavior, and robot are 0.84, 0.81, 0.78, and 0.79, respectively, with an overall mean precision of 0.81. Experimental results confirm the robustness and scalability of the proposed approach within HRC contexts, particularly in detecting unsafe behaviors arising from the improper or missing use of PPE. This study contributes to the development of intelligent safety monitoring systems, offering practical solutions to enhance safety compliance and foster sustainable HRC in complex industrial environments.

The maintenance and management of existing bridge infrastructure rely on conventional two-dimensional construction drawings, which remain largely unstructured and difficult to integrate into digital workflows, including Building Information Modeling (BIM). In prestressed concrete bridges, tendons and anchorage systems are documented across multiple drawings and sectional views, making manual interpretation time-consuming and error-prone. This paper presents an automated pipeline for detecting and semantically structuring structural elements in 2D bridge drawings. The approach combines deep learning-based object detection, classical computer vision for identifier-frame detection, optical character recognition, and spatial matching to identify elements, recognize identifiers, and link elements with their labels. The extracted information is integrated into an ontology-based knowledge graph, enabling SPARQL-based validation of missing identifiers, cross-view relationships, and section-level quantities. On 29 bridge drawings, detection achieved mAP@50 values of up to 97.4%, and text recognition reached 93.0% exact-match accuracy with a 2.2% character error rate.

Collaboration in Construction Business Process Management (CBPM) often suffers from inefficiency, fragmentation, and security concerns. Blockchain and Smart Contract (SC) offer potential solutions by enabling automation, transparency, and tamper-resistant records. However, adoption remains limited due to two critical gaps: (1) insufficient automation, as current SCs lack cascaded (interdependent) execution, and (2) insufficient adaptability, as existing SCs are non-upgradable, limiting responsiveness to workflow changes. This paper proposes a SC-CBPM framework addressing these gaps through three objectives: (1) Automate CBPM tasks and processes; (2) Develop Cascaded SCs to link interdependent tasks and enforce access control; (3) Develop Upgradable SCs to allow updates without data loss. The framework is validated through two scenarios: BIM-based design collaboration and payment automation, demonstrating feasibility and acceptable computational workability. Performance is evaluated through gas consumption and latency, ensuring deployment readiness. The main contribution is advancing blockchain from a static record-keeping tool to an adaptive workflow automation mechanism.

Building design is a complex process that requires multidisciplinary collaboration to develop design solutions that comply with various requirements and regulations However, in the early design stages, alternative design options are rarely documented in a structured manner, resulting in limited traceability, inefficient decision-making, and a loss of valuable knowledge. An innovative methodology for the systematic management of design variants is introduced in this paper. This methodology involves the enrichment of Building Information Modeling (BIM) element data with metadata and the enablement of graph-based comparison and reuse of alternatives. Consequently, the core of the presented approach is a Revit add-in that extracts Industry Foundation Classes (IFC) data and transforms it into labeled property graphs. In contrast to the conventional approach of maintaining full models, the system employs a delta-based storage strategy. This involves storing only differences between model variants, thereby reducing redundancy and enhancing performance, achieving up to nearly 50

Ensuring geometric accuracy in precast concrete production is becoming increasingly critical, especially for modular, tolerance-sensitive designs. This is particularly evident in segmental structures with dry joints, where even minor deviations can significantly affect assembly and structural performance. Within this context, a demonstrator for Digital Twin (DT)-based quality monitoring is developed using a modular precast segmental pedestrian bridge as a test case. High-resolution geometric data for individual segments are acquired via structured-light scanning and linked to digital representations of the segments. These are subsequently integrated into a DT of the overall structure, enabling the combined consideration of design and as-built information. The demonstrator illustrates how scan-based data can support the identification of geometric deviations and contribute to quality-related decision-making during production. In addition, the integration of measurement data into a DT environment is outlined, highlighting opportunities for improved traceability and consistency across production stages. Qualit & auml;tskontrolle auf Basis Digitaler Zwillinge: Sicherung der Pr & auml;zision bei BetonfertigteilenDie Gew & auml;hrleistung der geometrischen Genauigkeit bei der Herstellung von Betonfertigteilen gewinnt zunehmend an Bedeutung, insbesondere bei modularen, toleranzempfindlichen Konstruktionen. Dies zeigt sich besonders deutlich bei Segmentkonstruktionen mit Trockenfugen, bei denen bereits geringf & uuml;gige Abweichungen die Montage und die statische Leistungsf & auml;higkeit erheblich beeintr & auml;chtigen k & ouml;nnen. Vor diesem Hintergrund wird ein Demonstrator f & uuml;r die Digitale-Zwillings (DT)-basierte Qualit & auml;ts & uuml;berwachung entwickelt, wobei eine modulare Fu ss g & auml;ngerbr & uuml;cke aus Betonfertigteilen als Testfall dient. Hochaufl & ouml;sende geometrische Daten f & uuml;r einzelne Segmente werden mittels Streifenlicht-Scanning erfasst und mit digitalen Darstellungen der Segmente verkn & uuml;pft. Diese werden anschlie ss end in einen DT der Gesamtkonstruktion integriert, wodurch die kombinierte Ber & uuml;cksichtigung von Konstruktions- und Bestandsdaten erm & ouml;glicht wird. Der Demonstrator veranschaulicht, wie scanbasierte Daten die Identifizierung geometrischer Abweichungen unterst & uuml;tzen und zur qualit & auml;tsbezogenen Entscheidungsfindung w & auml;hrend der Produktion beitragen k & ouml;nnen. Dar & uuml;ber hinaus wird die Integration von Messdaten in eine DT-Umgebung skizziert, wobei M & ouml;glichkeiten f & uuml;r eine verbesserte R & uuml;ckverfolgbarkeit und Konsistenz & uuml;ber alle Produktionsstufen hinweg aufgezeigt werden.

Bridges are a crucial part of infrastructure, but many are in urgent need of maintenance. Digital methods like Building Information Modeling (BIM) and Digital Twinning can support this process but depend on digital models that are often missing for existing structures. Automating the reconstruction of these models from existing documentation, such as construction drawings, is essential to accelerate digital adoption. Addressing a key step in the reconstruction process, this paper presents an end-to-end pipeline for extracting bridge cross-sections from drawings. First, the YOLOv8 network locates and classifies the cross-sections within the drawing. The results are then processed by the segmentation model Segment Anything Model (SAM), which generates pixel-wise masks without requiring task-specific training data. This eliminates the need for manual mask annotation and enables straightforward adaptation to different cross-section types, making the approach broadly applicable in practice. Finally, a global optimization algorithm fits parametric templates to the masks, minimizing a custom loss function to extract geometric parameters. The pipeline is evaluated on 33 real-world drawings and achieves a median parameter deviation of −2.2 cm and 2.4 cm, with an average standard deviation of 35.4 cm.

Automated code-compliance checking based on Building Information Modeling (BIM) has received increasing attention in recent years. However, many existing approaches rely on manually formalized rules derived from non-machine-readable standards and building codes, which limits scalability and hampers adaptability to regulatory changes. This paper presents a framework for the automated extraction and formalization of building code information requirements by leveraging Level 4 Smart Standards encoded in the National Information Standards Organization (NISO) Standards Tag Suite (STS) in combination with the ISOProps ontology. The proposed procedure generates machine-interpretable validation rules using the Shapes Constraint Language (SHACL), which can be directly applied to BIM models to produce structured compliance validation reports. The framework is demonstrated through a proof-of-concept implementation that uses real-world building regulations to verify the compliance of calcium silicate masonry units based on IFC models. The results indicate a substantial reduction in manual rule-modeling effort while enabling consistent compliance assessment across multiple regulatory revisions. Overall, the proposed approach supports more maintainable, transparent, and scalable compliance checking workflows, contributing to the digitalization and automation of regulatory approval processes.