Proceedings of Digital Frontiers in Buildings and Infrastructure International Conference Series
International Conference on Digital Frontiers in Buildings and Infrastructure (DFBI2026) 30 March - 1 April 2026 Abstract Submission Deadline: 30 November 2025 Notification of Abstract Acceptance: 15 December 2025 Full paper submission: 15 February 2026 Early Bird Registration Deadline: 31 January 2026 Normal Registration Deadline: 15 March 2026 Conference: 30 March - 1 April 2026
A Review on the Relationship BetweenRheological Properties and 3D PrintingParameters for Optimizing Concrete Printabilityand Structural Performance
3D concrete printing (3DCP) has emerged as an innovative construction technique that enables the automated fabrication of structures without the need for formwork, offering advantages such as design flexibility, material efficiency, and improved safety. The successful implementation of 3DCP relies heavily on the rheological properties of the printing material, which govern key parameters such as pumpability, extrudability, buildability, and interlayer bonding strength. This review explores how key rheological parameters, including static and dynamic yield stress, plastic viscosity, and structural build-up rate, affect not only the flow and deposition of fresh concrete but also interlayer bonding strength and overall structural integrity. Additionally, this review examines the influence of printing parameters such as layer deposition time, nozzle size, and print speed on interlayer adhesion and the overall stability of the printed structure. By synthesizing experimental and numerical studies, this work provides a comprehensive guideline for evaluating and predicting the performance of 3D printed concrete through rheological testing, facilitating the optimization of material formulations and printing processes for enhanced printability and structural reliability.
Open Access Proceeding of Conference on Digital Frontiers in Buildings and Infrastructure Series
Copyright (c) 2025 Proceedings of Digital Frontiers in Buildings and Infrastructure International Conference Series
Comparison of Data Labelling Techniques for Automating Postcode Extraction in NLP-Supported Early-Stage Building Design
Data labelling is crucial for the success of Natural Language Processing (NLP) models, as the quality of labelled data directly affects model accuracy and performance. In early-stage construction design, automating the data extraction of textual data is essential for integrating physical and digital workflows. However, data labelling presents significant challenges, requiring careful trade-offs between time, cost, and accuracy to meet project-specific needs. This paper compares three primary data labelling techniques for postcode extraction from project documents: manual, rule-based, and hybrid machine learning approaches. A review of the seminal literature reveals that manual labelling delivers high accuracy and quality but is labour-intensive and better suited for small datasets or creating gold standards. Rule-based techniques, such as regular expressions (Regex), automate labelling for structured data using predefined patterns, offering efficiency but requiring domain expertise. Machine learning-driven methods, like Named Entity Recognition (NER), enable scalability for large datasets but often demand task-specific fine-tuning. Due to suboptimal NER performance in initial testing, a hybrid approach combining Regex with NER was developed and implemented using Google Colab. Through empirical evaluation of postcode extraction from construction project documents, the rule-based approach achieved 96.7% accuracy when compared against manual labelling as the gold standard, while the hybrid machine learning approach achieved 98% accuracy. This paper provides a comparative framework to guide practitioners in selecting the most appropriate data labelling technique based on their specific needs, balancing accuracy, efficiency, and scalability to optimise workflows and enhance automation in early-stage building design.
Exploring Virtual Reality for Enhancing Perceived Indoor Environmental Comfort in Educational Buildings: A Scoping Review
This scoping review systematically explores the role of Virtual Reality (VR) in assessing and enhancing perceived indoor environmental comfort within educational buildings. The main objective was to map existing literature addressing VR applications across thermal, visual, acoustic, spatial, general environmental comfort, and indoor air quality dimensions. Given the inconsistent user satisfaction with indoor environmental quality (IEQ) in educational spaces, this study highlights VR's potential as a cost-effective tool for assessing and optimizing occupant comfort post-construction. Employing the Joanna Briggs Institute (JBI) methodology, structured searches were conducted in the Scopus database, resulting in 1,125 unique records. After rigorous screening based on predefined criteria, 17 peer-reviewed journal articles published between 2014 and 2024 were included. Findings demonstrate VR's diverse effectiveness in comfort assessments, educational interventions, and informed design decision-making processes. Visual comfort was the most frequently studied dimension, with VR reliably simulating optimal lighting conditions but showing limitations in dim or high-contrast scenarios. Thermal comfort simulations effectively enhanced students' understanding of complex concepts. Acoustic comfort evaluations highlighted beneficial impacts on cognitive performance, while biophilic VR environments improved general comfort, reducing stress indicators. Notable limitations include small participant samples, technological realism constraints, limited multisensory integration, and reliance on a single database. This review uniquely contributes to architectural practices by providing insights for informed design, educational enhancement, and policy-making, ultimately promoting occupant well-being, sustainable design, and improved educational environment.
Contribution to the automatic recording and documentation of crossing events through the combination of BIM and SHM
This contribution presents a practical use case demonstrating how Building Information Modelling (BIM) can be combined with Structural Health Monitoring (SHM) to support bridge management during the operation phase. A Python-based algorithm processes laser sensor data to automatically detect vehicle crossings and derive additional information such as direction and speed. This data is documented in BIM and used for event visualization. The approach illustrates the potential of integrating BIM and SHM for digitalizing routine tasks and extending existing methods, such as construction process simulation, into the operation and maintenance phases.
Integration of UAV Photogrammetry and AI-based Image Recognition for Investigating Occupation of Irrigation Public Lands
This study presents an innovative approach combining Unmanned Aerial Vehicles (UAVs) and AI-based image recognition to investigate unauthorized occupation of irrigation public lands. Traditional land survey methods are labor-intensive, time-consuming, and often constrained by inaccessible terrain. By integrating UAV-based photogrammetry and deep learning techniques—particularly the YOLOv7 object detection framework—this research enables efficient acquisition of high-resolution orthophotos and automatic detection of land use violations. Case studies from southern Taiwan demonstrate improved survey accuracy and significant time savings compared to manual inspection. This method provides a scalable, repeatable framework for land resource monitoring and supports more transparent and standardized land management operations.
Digital Twin for Positive Energy Districts: A Web-Based Viewer for Scenario Simulation of the mixed-use area Jättesten, Gothenburg
The transition to Positive Energy Districts (PEDs) requires advanced digital tools for modeling, simulation, and optimization at the urban scale. This study presents a proof-of-concept Digital Twin web-based viewer that integrates Grasshopper scripting with EnergyPlus and UrbanOpt to evaluate PED scenarios. EnergyPlus enables detailed energy simulations at the building-level, while UrbanOpt extends analysis to district-level energy systems. The web viewer offers an interactive interface for real-time visualization of simulations, scenario comparisons, and performance metrics. This integrated framework supports the modeling of energy efficiency measures, renewable energy integration (e.g., photovoltaics and battery storage), and flexibility strategies such as demand-side management. The Jättesten district in Gothenburg, Sweden, serves as the primary case study, demonstrating real-time scenario testing through interactive visualization and data-driven analytics. By merging urban datasets with parametric modeling, the Digital Twin acts as a decision-support tool for municipalities, planners, and facility managers. While the framework’s scalability and integration with existing urban platforms require further development, and its reliance on detailed data and computational resources may limit adoption, this research underscores the potential of Digital Twins to bridge computational modeling and real-world energy planning in PED development.
A Simulation-Based Framework for Enhancing the Resilience of Maintenance Operations in Critical Infrastructure
Resilience of maintenance operations in critical infrastructure present a significant challenge for future communities, particularly as the frequency and severity of human-made and environmental disruptions continue to escalate. This paper highlights the role of maintenance operations in ensuring the functionality of essential infrastructure components, with a focus on enhancing their resilience to ensure operational continuity. While existing research on improving resilience in maintenance operations remains limited, this study aims to bridge this gap by developing a conceptual framework that employs a simulation-based approach to improve decision-making in complex maintenance scenarios. The proposed framework is adapted from Bruneau et al. (2003) and grounded in the 4Rs resiliency theory. The framework is then applied to the case study of maintaining hospital elevators, focusing on the specific challenges and needs associated with maintaining this critical infrastructure. By integrating these principles, the proposed framework seeks to ensure that critical infrastructure remains efficient, reliable, and resilient in the face of unforeseen disruptions, ultimately supporting the long-term sustainability of essential services.
Ontology-Driven Project Management: A Framework for Structured Data and Automation
Construction project management grapples with significant complexity and knowledge integration challenges, often hindering efficient decision-making. This paper presents an ontology-oriented programming paradigm designed to model construction projects, providing a semantically rich framework for capturing domain knowledge. The methodology centers on the development of a project management ontology mirroring Work Breakdown Structure (WBS) principles. This involves hierarchically organizing core concepts like Project, Task, Resource, and Deliverable. Key relationships governing task dependencies, hierarchical structures, resource assignments, along with essential task attributes including temporal constraints and cost information, are formally defined using OWL classes and properties. This conceptual ontology was implemented using the Owlready2 Python library, facilitating structured data representation and manipulation. The paper further explores the practical application of this ontology framework through five distinct use cases: enhanced progress monitoring, data-driven informed decision-making, integrated Building Information Modeling (BIM), automated compliance checking, and synergistic Artificial Intelligence (AI) integration. A supporting workflow is also proposed. This research highlights the potential of structured ontologies, realized via tools like Owlready2, to significantly improve knowledge representation, system interoperability, and overall decision support effectiveness within the demanding construction domain.
An Information Integration Platform for Visualizing Campus Sustainability with Digital Twin—A Case Study of National Taiwan University
Universities are increasingly expected to demonstrate social responsibility and reduce environmental impacts as part of their commitment to sustainable development. However, the complexity and diversity of campus facilities and stakeholders make managing sustainability initiatives a significant challenge. Digital dashboards have emerged as effective tools for monitoring environmental performance, identifying issues, and enhancing communication among stakeholders. Despite their potential, the development and implementation of such dashboards are often hampered by fragmented data collection, integration, and management across various administrative units and user groups. This study addresses these challenges by proposing a robust system framework designed to streamline data management and support the visualization of campus sustainability efforts. Using National Taiwan University as a case study, we developed an information integration platform capable of organizing, integrating, and managing a wide range of general and environmental data types from multiple sources. The platform’s multi-level data structure and user management modules enable tailored access and visualization for different stakeholders, strengthening data governance and fostering a culture of information sharing. Ultimately, the proposed framework contributes to a more sustainable and transparent campus environment, supporting strategic decision-making and advancing the university’s sustainability goals.
Construction Quality Inspection by Local Point Cloud to BIM Registration and Component Association Based on Vision Foundation Model
Construction quality inspection is essential for verifying conformity between physical installations and design specifications. Traditional inspection methods rely heavily on manual comparison between construction drawings and onsite conditions, making the process labor-intensive, subjective, and lacking in digital traceability. Although laser scanning technologies have been widely adopted for capturing as-built data, the automated registration of local LiDAR scans to BIM models and the association of onsite components with their designed counterparts remain underdeveloped. To address these challenges, this study proposes a novel framework for local point cloud to BIM registration and component association, leveraging vision foundation models to enable component-level inspection directly on construction sites. The proposed framework comprises four main stages. First, raw LiDAR scans are preprocessed and converted into binary visibility maps centered on the LiDAR sensor. Second, synthetic visibility maps are generated by virtually scanning the BIM model from corresponding viewpoints. Third, both real and synthetic visibility maps are encoded using a vision foundation model to extract high-dimensional features. These features are compared to generate similarity maps for coarse localization, which is further refined using the Iterative Closest Point (ICP) algorithm to achieve accurate registration. Finally, onsite components are identified using the vision foundation model and matched to their corresponding BIM components through bipartite graph matching, solved via the Hungarian algorithm. Experiments conducted on real-world construction sites demonstrate the robustness and accuracy of the proposed method, validating its effectiveness for practical applications in construction quality inspection and digital twin development
Enabling Digital Transformation in Construction: A Framework for Integrating BIM and VR in Early Design Phases
Integrating Building Information Modeling (BIM) and Virtual Reality (VR) has transformative potential to enhance communication among stakeholders in the early design phases of construction projects. However, architecture, engineering, and construction (AEC) firms still encounter significant challenges, including high initial investment costs, software interoperability limitations, fragmented data workflows, and insufficient workforce training programs. This study conducts a systematic literature review (SLR) to identify and analyze the most critical barriers to BIM-VR adoption. Based on these insights, this research develops a comprehensive framework to mitigate these challenges and facilitate the practical, scalable integration of BIM-VR in construction projects. The framework outlines key strategies and best practices, ensuring digital innovations are accessible even to firms with limited resources. This research contributes to advancing digital construction methodologies, improving industry-wide efficiency, and facilitating the seamless adoption of BIM-VR technologies in real-world projects by providing a structured and actionable approach to overcoming BIM-VR implementation barriers. The findings offer significant implications for academia and industry, fostering more cost-effective, collaborative, and technologically advanced construction practices
BIM, Virtual Reality, Miscommunication, Early Design Phases, Adoption Challenges, Framework Development
Big Data and AI in Urban Planning: Advancing Pedestrian-Oriented Development in India – “Challenges, Strategies, and Future Directions
The integration of big data and artificial intelligence (AI) in pedestrian-oriented urban planning presents transformative potential for sustainable cities, yet implementation in developing nations like India remains constrained. This study examines global applications of big data and AI in pedestrian planning, focusing on India's emerging but limited adoption through a systematic literature review of 76 publications (2014–2025). While advanced economies leverage AI for walkability assessments, pedestrian safety, and infrastructure optimization using multi-source data, India's efforts are predominantly restricted to vehicular safety projects like iRASTE and BATCS. Key barriers include fragmented data ecosystems, reliance on manual methods, and inadequate pedestrian-centric datasets in platforms like NUDM and IUDX. Challenges such as temporal-spatial data misalignment, privacy concerns, and algorithmic biases further hinder progress. The study proposes a three-pronged framework for India: ethical data governance models balancing innovation and privacy; hybrid methodologies combining AI tools with traditional surveys; and public-private partnerships to build local capacity. Successful case studies from Nagpur and Bengaluru demonstrate scalable opportunities, while global benchmarks highlight the need for context-sensitive adaptations. A novel crowdsourced GPS-camera system is suggested to capture pedestrian perspectives, addressing critical data gaps. Findings emphasize AI adoption in pedestrian planning transcends technological advancement it necessitates institutional collaboration, policy standardization, and equity-focused solutions aligned with SDG 11. Strategic recommendations include longitudinal pilot studies, regulatory reforms, and integrated data hubs to bridge the gap between India's smart city initiatives and inclusive pedestrian infrastructure. The study positions India to lead Global South nations in human-centric urban innovation through responsible AI deployment.
Big data, Artificial intelligence, Pedestrian planning, Walkability, Urban infrastructure, SDG 11
Identification of Challenges in the Application of Artificial Intelligence in Facility Management and Highlighting Optimization Potentials
Artificial Intelligence (AI) and data collection in facility management bring a variety of improvements, such as increased efficiency, cost control, enhanced user experiences, and more. With targeted data management, this information can be further processed and contribute to the sustainable operation of buildings. AI currently presents several challenges that prevent its optimization potential from being fully realized. These challenges can be specified across various data domains, such as data quality, data security, and more. Additionally, legal aspects, such as the collection of user data and the associated legal restrictions, play a significant role. To identify and address these challenges, an Ishikawa diagram is created. The Ishikawa diagram is used to systematically analyze possible problems. This method helps visualize connections, prioritize causes, and identify approaches to solutions. During its application, the problems are first defined, and their impacts are analyzed. Subsequently, the main categories of causes are determined, such as people, machines, methods, and so on. Afterward, the causes are identified through brainstorming, followed by an analysis of these causes. Based on this analysis, appropriate measures can be derived. These measures may include avoidance, mitigation, acceptance, and transfer. The results are then used to eliminate or minimize the causes of the challenges. The results and corresponding measures are documented and reviewed to determine whether the problem can be resolved or reduced. The outcomes are presented in the form of a table, linked with the respective measures, and the proposed solutions are outlined.
A Scoping Review of Digital Technologies used for Automating Onsite Construction Inspection: Applications and Future Direction
Construction inspection is an important aspect of project delivery in the Built Environment. However, on construction sites, the process of compliance checking is largely conducted manually. In person inspections have been found to have error rates of 20-30%. These inefficiencies have resulted in significant quality impacts, cost overruns and schedule delays. In the context of these challenges, coupled with the demand for remote inspections during the pandemic, there has been increasing research into digital technologies for the automation of construction compliance. However, to date, many developments happened in isolation focusing on narrow areas. There has been no overarching review available to consolidate the gamut of digital solutions that can be used in construction inspection, evaluate their practical applications and consider how they fit within the wider compliance workflow. To fill this gap, this paper conducted a scoping review to identify the key digital technologies with the potential to automate inspection tasks on construction sites. The study analysed 136 papers published in the last five years to identify the development trajectories of digital solutions in construction compliance and to suggest possible directions in which certain technologies can be further applied to enhance construction compliance. The findings reveal key inspection themes where technology can enhance construction compliance namely defect detection, dimension measurement, and alignment. Additionally, technology stack i.e. combination of hardware and software most compatible to serve each inspection purpose was analysed. The study proposes a roadmap through technology-inspection matrices to guide commercial deployment and future research in underutilised tech combinations.
Knowledge-enhanced Vision-Language Models for Few-Shot Object Detection in Construction Site
Visual understanding of complex construction site objects is critical for project safety management and worker-robot collaboration within the construction domain. However, deploying deep learning algorithms on construction sites presents significant challenges due to high data annotation costs, substantial computational requirements, and the absence of large-scale training datasets. While large-scale pre-trained multimodal foundation models have shown success in natural language understanding and visual recognition, their application in construction safety management remains limited because of the need for domain-specific knowledge. To address these challenges, this paper proposes a knowledge-enhanced multimodal learning approach for few-shot object detection in construction scenarios. The proposed method comprises two components: (1) leveraging existing semantic knowledge in the construction domain to detect potential objects in construction scenes using a template matching approach; and (2) introducing a multimodal image semantic recognition method that integrates visual and textual knowledge specific to the construction field. We evaluate our approach on the AIMDataset. The results demonstrate that, without network training or large-scale construction samples, our method can achieve effective object detection under few-shot conditions using only existing models and a small amount of provided visual and textual knowledge. This approach highlights its potential for applications in construction scenarios.
Construction sites, object recognition, knowledge-enhanced, vision-language models, few-shot learning
Discrete Design in Architecture: An Integrated Design-to- Assembly System
This paper explores the integration of digital discretisation strategies with robotic assembly to advance architectural design and production within the framework of Design for Manufacturing and Assembly (DfMA). Discretisation—understood as a digitally driven method of breaking down design into modular, adaptable parts—enables flexible, scalable systems where components can be modified without compromising structural coherence. It supports precision, efficiency, and sustainable construction practices. As part of an ongoing research project on discrete design in DfMA, this paper builds on two previously developed classification systems derived from the parametric reconstruction of built and academic case studies (ZamaniGoldeh, Dounas, & Agkathidis, 2025). The methodological classification—Computational Growth, Subdivision Surfaces, and Cross-Sectional Techniques—focuses on geometric discretisation methods. The strategic classification—Top-Down, Bottom-Up, and Hybrid—centers on the modelling logic and design progression. To validate these systems, the research employs a dual approach: digital simulation and physical prototyping. Algorithms are implemented through robotic simulations in Grasshopper. Subsequently, a selection of discrete prototypes is physically assembled, allowing for assessment of joints, accuracy, stability, and constructability. This paper provides insights into the assembly logic of discrete design systems and highlights the potential of digital fabrication workflows to transform architectural practice. Going forward, it offers valuable learnings for DfMA from design to assembly and lays the groundwork for further physical validations and scaled modelling. The outcomes reinforce the practical relevance of the classification systems and offer a foundation for future, real-scale applications of robotic-based architecture.
Affordable Housing: A Complex Adaptive Systems Perspective
Affordable housing remains a critical challenge shaped by complex economic, social, and political forces. Traditional approaches often struggle to address the deep, interconnected dynamics involved in affordable housing. These methods tend to overlook the shifting relationships between key players, such as policymakers, communities, and market actors, and how their actions influence one another over time. This study explores a different way of thinking about the affordable housing sector, by applying the concept of Complex Adaptive Systems (CAS). CAS offers a way to understand housing not as a fixed structure but as a living, evolving system made up of many different actors, like policymakers, developers, communities, and market forces, that influence each other in real-time. By applying CAS principles, this paper aims to support better decision-making and policy design. It offers a way to understand how small changes, like shifts in behaviour or policy, can ripple through the system and create broader impacts. This dynamic view helps identify real-time effects of interventions and improves how strategies are developed. The study begins by outlining the main principles of CAS, emphasizing the importance of thinking in terms of complexity rather than straight-line cause and effect. It then draws connections between these ideas and the realities of the affordable housing sector, showing how the CAS framework can be practically applied. The paper concludes that using a CAS lens opens new pathways for creating flexible, innovative policies that can better address housing shortages and promote long-term affordability.
Data-driven approaches to improve Indoor Environmental Quality: A Systematic Literature Review.
Indoor environmental quality (IEQ) refers to the conditions within a building and is a key factor in occupant comfort. To improve these IEQ, heating, ventilation, and air-conditioning (HVAC) systems are currently being used in indoor environments in buildings. However, inefficient systems to improve IEQ lead to high energy consumption, which is directly connected to building-energy-related CO2 emissions. Unfortunately, these emissions are inextricably linked to global climatic issues. Data-driven technologies show a positive future in solving climate-related targets. Technologies such as artificial intelligence (AI), machine learning (ML), and digital twin (DT) are being explored to improve energy performance while improving IEQ. Therefore, this paper aims to systematically review the applications of data-driven technologies for improving IEQ and energy efficiency. A comprehensive literature search using the PRISMA method to retrieve publications related to data-driven and IEQ. The findings highlight common areas of data-driven applications in IEQ, including the detection and prediction of IEQ factors, IEQ controls, building energy management, and HVAC system controls. Commonly used ML and AI techniques identified, including deep learning, neural networks, KNN, SVM, decision trees, and multi-objective algorithms. AI in combination with BIM and IoT techniques was employed in the studies to develop DT models for real-time building monitoring. Further, the review highlights barriers to data-driven and digitalisation approaches in buildings that impede the robust approaches to IEQ improvement.
Indoor environmental quality, energy efficiency, machine learning, artificial intelligence, digital twin
Seismic Risk Assessment for Critical Infrastructure Using BIM: A Component-Level Approach
Building Information Modeling (BIM) has become a key technology in engineering projects, encompassing structures such as buildings, bridges, pipelines, and road networks. Despite its widespread adoption, its application in seismic risk assessment for infrastructure systems remains underexplored. This study proposes a BIM-based framework designed to evaluate seismic risk by utilizing detailed digital representations of both structural and non-structural elements. By integrating fragility curves, the framework enables component-level vulnerability analysis, where each element is assigned specific fragility parameters, including median capacity and standard deviation across various damage states. The model facilitates the simulation of different seismic scenarios, allowing for the rapid evaluation of infrastructure performance and the identification of the most at-risk components. This methodology provides engineers and decision-makers with a robust tool for quantifying seismic risk and developing strategies to enhance resilience. Preliminary results indicate that BIM significantly improves the efficiency of seismic risk assessment through structured data management and enhanced visualization capabilities, ultimately aiding in better-informed resilience planning.
Free-form Grid Structure Form Finding based on Machine Learning and Multi-objective Optimisation
Free-form structural designs are reshaping modern architecture with their expressive aesthetics and novel spatial configurations. However, their digital transformation still encounters obstacles, particularly in aligning fluid geometries with rigorous structural requirements, and in assembling high-quality datasets suitable for advanced machine learning (ML) methods. Traditional form-finding approaches often neglect critical factors—such as material behavior, load paths, and construction logistics—resulting in discrepancies between conceptual 3D models and built outcomes. To overcome these challenges, this paper presents a novel pipeline that integrates ML with multi-objective evolutionary optimisation, using glued laminated timber (GLT) as a case study. Central to our method is a Transformer-based neural network that harnesses NURBS representations of 3D geometry, creating a structured dataset for curvature prediction. These ML-generated forms are then refined through an evolutionary optimisation process targeting minimal structural mass, stress, and strain energy. Experimental results show notable improvements in design performance, with reductions in mass (3.6%), stress (up to 15%), and strain energy (68% under mesh load) . This synergy of ML-driven geometry and robust optimisation significantly advances digital construction practices by fostering a data-driven, automated workflow for complex free-form design. Practical implications extend across the building and infrastructure sectors, enabling better alignment between conceptual design and final construction, lowering material consumption, and mitigating deviations during fabrication. By coupling aesthetic exploration with structural rigour, the framework underlines a crucial step toward more sustainable, efficient, and constructible free-form structures.
Evaluating the Adoption of XR Technologies for High-Risk Industrial Training Application
This paper investigates the effectiveness of a virtual reality training environment (VTE) compared to traditional training methods employed for industrial application, specifically high-voltage switching procedures. Beyond direct comparison, the research explores how eXtended Reality (XR) technologies can combine with gamification practices to complement conventional training and enhance overall learning outcomes. Four experimental groups are assessed: (1) traditional training only, (2) VR training only, (3) traditional training followed by VR, and (4) VR training followed by traditional methods. Participants’ baseline knowledge, skills and confidence are measured with a pre-training assessment. Post-training assessments are used to compare the effectiveness of the different training methods. For the groups using VR, immersive tendencies questionnaires (ITQs) will be used to measure trainees’ predispositions to immersion, and presence questionnaires (PQs) will be used to measure trainees’ sense of presence within the VTE. Statistical analyses evaluate performance differences, training order effects, and potential synergies between VR and traditional methods. The study aims to determine whether integrating VR with traditional methods enhances knowledge retention, training efficacy, and participant confidence. Findings will contribute to the understanding of VTEs for safety-critical industries.
Designing Workspaces For Success: A Refined Activity-Based Workspace Model for Boosting Employee Productivity
Activity-Based Workspace (ABW), which offers a variety of space options tailored to different professional activities, is becoming the standard for companies seeking to enhance both employee productivity and space optimization. However, despite their growing adoption, challenges such as time lost in finding suitable workspaces, reduced privacy, and limited personalization raise concerns about whether the ABW model effectively balances employee well-being with spatial efficiency. We argue that one of the intrinsic constraints in implementing ABW is that workplaces accommodate diverse job functions, each with distinct spatial requirements that do not necessarily align. To address this, we introduce a data-driven decision-making model designed to facilitate ABW, evaluating the specific needs of different job categories within organizations. Our research is conducted in collaboration with employees from Mantu, a global consulting company with 8,500 professionals in the service sector. We propose a cosine distance based matching model to determine the best mix of workspace typologies for different job categories based on their activities. In this paper, we present the findings for a sample of four job category clusters: IT, Consultancy, Sales & Marketing, and Finance. The alignment is made with five workspace typologies classified based on their degree of enclosure (open, closed, semi-closed) and their suitability for individual, group, or hybrid work. The match is guided by employee-voted criteria, including autonomy, collaboration, creativity, concentration, oral/visual privacy, and confidentiality.
Significant critical success factors for the implementation of safety programs in the construction industries of developing countries
The construction industry is widely recognized as one of the most hazardous sectors, with accident rates on construction sites significantly higher than in other industries. This study aims to identify the critical success factors that influence the effective implementation of safety programs in the construction industry, particularly in developing countries. To achieve this aim, Iraq was selected as a case study of a developing country, where both semi-structured interviews and questionnaire surveys were conducted with construction experts to identify and rank the critical success factors. This paper highlights the importance of these critical success factors in enhancing the implementation of safety programs. It provides valuable insights for policymakers, project managers, and industry stakeholders to develop more effective safety strategies, improve regulatory frameworks, and foster a safer working environment across the construction sector in developing regions.
Exploring the Use of Machine Learning in Enhancing BiddingDecisions for Construction Projects
This study addresses the critical need to enhance competitive bidding strategies in construction by revisiting Friedman's 1956 bidding model and incorporating Ioannou's revised equations to improve predictive accuracy. The research fills a significant gap in integrating machine learning techniques into bidding theory, which offers a data-driven approach to optimize bid decision-making. Using synthetic data, logistic regression served as a baseline model, while Random Forest classifiers outperformed with 98% accuracy by addressing class imbalance and effectively capturing the non-linear relationships among key variables, such as reserve price and bid-to-cost ratio. The findings revealed that machine learning models could simplify complex bidding theories and provide contractors with actionable insights, supporting bid or no-bid decisions. However, reliance on synthetic data limits the generalizability of these results. Future work should focus on validating the proposed models using real-world bidding datasets and exploring advanced techniques, such as ensemble methods, to enhance predictive performance. This study underscores the potential of machine learning to transform traditional bidding practices, which offers both theoretical advancements and practical implications for construction management.
Development of an Indoor localization and positioning system in non-GPS environment using standalone smart phones and its implementation in construction site photo management application.
This research paper presents a novel indoor localization and positioning system designed for GPS-denied environments, specifically construction sites. It addresses the challenge of self-positioning on construction sites, where GPS signals are unavailable, such as in tunnels, buildings, and underground areas. We developed a high-precision self-positioning algorithm using a smartphone's camera and sensors to track location without external signals. The system combines a camera-based algorithm that tracks image pixel movement with augmented reality (AR) and smartphone sensors (accelerometer, gyroscope, magnetometer, and barometer).Unlike existing technologies like beacons, Wi-Fi, UWB, markers, and SLAM, which often lack accuracy, our system achieves centimeter-level precision for indoor positioning. The system is integrated into a construction site photo management app that automatically tags photos with accurate location data, including latitude, longitude, and the camera's orientation (roll, yaw, and pitch). The app places a pin on a map showing the camera's direction, ensuring precise geotagging. This system enhances construction site productivity by providing reliable documentation, essential for tracking and managing site progress. Geotagged images and metadata can be used for site inspections, progress monitoring, and decision-making. The research demonstrates the effectiveness of combining smartphone-based camera algorithms, AR, and sensor fusion to solve indoor positioning challenges in construction environments. This system have been tested in several construction sites and confirmed the accuracy of centimeter (cm) precision. The system's high precision and real-time capabilities offer a groundbreaking solution to the limitations of current indoor positioning technologies used in the construction sites.
AI Agents for Automated Design and Compliance Verification in Buildings and Infrastructure: A Case Study Approach with No-Code/Low-Code Implementation
The buildings and infrastructure sector faces increasing demands for accuracy, efficiency, and regulatory adherence. Manual design calculations and compliance checks are slow and error-prone. This paper presents a case study-driven approach to automating these tasks using AI agents within a no-code/low-code workflow automation framework. We examine three applications: ASHRAE 62.1 ventilation rate calculations, ADA accessibility compliance for door clearances, and structural foundation sizing. The methodology utilizes a visual workflow automation platform to extract information from building codes, perform calculations, and validate results with integrated Large Language Model (LLM)-based quality assurance checkpoints. For each case, we document the agent design, integration methodology, quality assurance protocols to manage hallucination risks, and quantifiable outcomes. The implementation of LLM-based QA/QC checkpoints ensures robustness by validating that information extracted from codes is correct and calculations are performed accurately, enhancing trust in AI-driven workflows. Results from three real-world case studies demonstrate an 80% reduction in task completion time and substantial decrease in calculation errors. Beyond efficiency gains, AI agents enhance resilience by streamlining design processes, minimizing regulatory non-compliance risks, reducing dependency on manual expertise, and adapting dynamically to evolving standards and design requirements. This approach democratizes digital transformation in buildings and infrastructure, making design calculation and compliance verification tools accessible to both large and small firms without specialized AI expertise. The integration of LLM validation addresses a critical challenge in adopting AI for safety-critical applications. Future directions include expanding AI agent capabilities for more complex design scenarios and integrating real-time data sources for dynamic compliance monitoring.