Search Results (35)

The real estate industry, known for its complexity and exposure to systemic and idiosyncratic risks, requires increasingly sophisticated investment risk assessment tools. In this study, we present the Real Estate Construction Investment Risk (RECIR) model, a machine learning-based framework designed to quantify and manage multi-dimensional investment risks in construction projects. The model integrates diverse data sources, including macroeconomic indicators, property characteristics, market dynamics, and regulatory variables, to generate a composite risk metric called the total risk score. Unlike previous artificial intelligence (AI)-based approaches that primarily focus on forecasting prices, we incorporate regulatory compliance, forensic risk assessment, and explainable AI to provide a transparent and accountable decision support system. We train and validate the RECIR model using structured datasets such as the American Housing Survey and World Development Indicators, along with survey data from domain experts. The empirical results show the relatively high predictive accuracy of the RECIR model, particularly in highly volatile environments. Location score, legal context, and economic indicators are the dominant contributors to investment risk, which affirms the interpretability and strategic relevance of the model. By integrating AI with ethical oversight, we provide a scalable, governance-aware methodology for analyzing risks in the real estate sector. Full article

Information Systems (ISs) research frequently relies on digital trace data, often using simple activity counts as proxies for complex latent constructs like ‘experience’. However, the validity of such proxies is often assumed rather than critically scrutinized. This study problematizes this practice by treating a common proxy—a creator’s prior project count on Kickstarter—not as a measure of experience, but as a focal signal whose meaning is inherently ambiguous and context-dependent. By analyzing large-scale data (N ≈ 16,407 projects), we uncover a nuanced ‘experience paradox.’ The proxy exhibits a significant inverted-U association with backer mobilization and non-linearly moderates the value of other positive signals. Strikingly, it also maintains a persistent negative direct association with total funding, with its meaning varying significantly across project categories. These findings reveal the profound ambiguity of seemingly objective digital traces. Our primary contribution is methodological and theoretical: we provide a robust empirical critique of naive proxy use and refine signaling theory for digital contexts by integrating it with cognitive limitations and contextual factors. We urge IS scholars to develop more sophisticated measurement models and offer specific, evidence-based cautions for platform managers against the simplistic use of activity metrics in the digital economy. Full article

The burgeoning economic relationship between Türkiye and Poland, marked by a targeted $10 billion trade volume, has catalyzed significant Turkish engagement in the Polish construction sector. Ranked second globally in international contracting, Turkish firms are increasingly undertaking complex infrastructure projects in Poland, making it a critical European market to analyze. This study develops a comprehensive framework to identify and evaluate the sources of sustainable competitive advantage for Turkish contractors operating in this dynamic environment. The research adopts a qualitative, single-case study methodology, centered on the extensive project portfolio of a leading Turkish firm in Poland. The analytical approach is twofold. First, it employs Porter’s Diamond Framework to deconstruct the existing competitive advantages, revealing a shift from traditional low-cost models to a sophisticated synergy of superior labor management capabilities, strategic local partnerships, and expertise in complex project delivery. These strengths are shown to align directly with Poland’s critical needs, particularly its skilled labor shortage and ambitious infrastructure agenda. Second, a Foresight Analysis is conducted to map plausible future scenarios through 2035, addressing key uncertainties such as geopolitical shifts and the pace of technological adoption. The findings demonstrate that the sustained success of Turkish contractors hinges on their ability to deliver targeted value. The study concludes by proposing a set of “no-regrets” strategies—including accelerated ESG and digital up-skilling, forging deep local partnerships, and developing financial engineering capabilities—designed to secure and enhance their competitive positioning. The results provide an actionable roadmap for industry practitioners and valuable insights for policymakers fostering bilateral economic collaboration. Full article

Highway alignment optimization is critical for developing sustainable and resilient transportation infrastructure. Traditional alignment selection methods frequently fail to comprehensively account for all of the diverse factors, including geometric compliance, traffic efficiency, land use factors, environmental impacts, and cost considerations, ultimately resulting in suboptimal project outcomes. To address these challenges, this study proposes a building information modeling (BIM)-based alignment optimization framework that integrates diverse datasets, sophisticated modeling techniques, and stakeholder collaboration. The proposed framework systematically enables the user to model terrain, design geometric features, simulate traffic, and conduct cost analysis and environmental impact assessments. A case study of the Dera Ghazi Khan Northern Bypass project in Pakistan, a critical infrastructure project designed to ease congestion and enhance regional connectivity, is presented to validate the proposed framework. Three alignment alternatives were analyzed, with the optimized solution (Alignment Option 2) demonstrating a 30% reduction in congestion, a 20% decrease in travel time, and a 6.48% reduction in construction costs compared to the other alignment alternatives. These outcomes highlight the transformative potential of BIM-driven optimization to significantly enhance sustainability, cost-efficiency, and operational performance. This framework offers a scalable and adaptable model to guide future infrastructure development initiatives toward more sustainable outcomes. Full article

The architecture, engineering, and construction (AEC) industry is experiencing significant digital transformation, creating a critical need to understand how future professionals perceive and accept emerging technologies. This study applies the Technology Acceptance Model (TAM) to investigate undergraduate construction students’ perceptions of Building Information Modelling (BIM) and examines how these factors influence their views on BIM applications. Using an exploratory mixed-methods approach, we analysed 773 responses from students at an Australian university across AEC disciplines, with 607 providing substantive qualitative feedback. Qualitative thematic analysis provided rich contextual understanding of student perspectives, while quantitative analysis revealed pattern frequencies across disciplines. Findings showed that perceived usefulness (PU) (37.7%) and attitude toward using (ATU) (68.4%) dominated student responses, while perceived ease of use (PEOU) (6.9%) received less attention. Productivity benefits (15.3%) and increased accuracy (7.9%) emerged as primary usefulness drivers. Disciplinary differences were significant, with Civil Engineering students emphasising design validation aspects and Construction Management students focusing on project delivery benefits of BIM. Notably, students exhibited sophisticated ambivalence, recognising BIM’s professional value while expressing concerns regarding the steep learning curve, especially when its adoption is coupled with the integration of emerging technologies such as artificial intelligence. This study contributes to the existing knowledge by: (1) documenting the current state of student perceptions in BIM education; and (2) revealing the complex interplay between technological enthusiasm and socio-professional concerns across both educational and industry settings. These findings provide evidence-based guidance for developing BIM curricula that address both socio-technical competencies and student perceptions, helping bridge the gap between educational outcomes and students’ understanding of industry requirements. Full article

This research investigates how historical architectural practices offer valuable solutions for contemporary climate adaptation challenges. Through systematic documentary analysis, we examine how European builders across centuries developed sophisticated construction techniques to address climate variability—techniques that remain relevant as we face increasingly extreme climate conditions. Our study focuses mainly on La Aljafería Palace in Zaragoza, Spain, a remarkable 11th-century Islamic structure that exemplifies bioclimatic design principles. We analyze its ingenious architectural elements—strategic courtyards, thermal mass management, passive ventilation systems, and innovative water features—that collectively create comfortable interior environments despite the region’s harsh summer climate. Similar analyses were conducted on historical structures in Italy, Greece, Portugal, and Cyprus as part of the ClimAid European project. Our findings reveal that these ancestral building practices utilized locally available materials and passive design strategies that required minimal energy inputs while providing effective climate regulation. We conclude that modern architects, conservationists, and policymakers face a dual challenge: developing strategies to reduce the vulnerability of historical structures to current climate impacts while also learning from and adapting these time-tested techniques to contemporary sustainable design. This research demonstrates how cultural heritage can serve not merely as an object of preservation but as a valuable knowledge repository for addressing present-day environmental challenges. Full article

Accelerated urbanization in China poses significant challenges for developing urban planning strategies that are responsive to diverse climatic conditions. This demands a sophisticated understanding of the complex interactions between 3D urban forms and local climate dynamics. This study employed the Conditional Generative Adversarial Network (cGAN) of the Pix2Pix algorithm as a predictive model to simulate 3D urban morphologies aligned with Local Climate Zone (LCZ) classifications. The research framework comprises four key components: (1) acquisition of LCZ maps and urban form samples from selected Chinese megacities for training, utilizing datasets such as the World Cover database, RiverMap’s building outlines, and integrated satellite data from Landsat 8, Sentinel-1, and Sentinel-2; (2) evaluation of the Pix2Pix algorithm’s performance in simulating urban environments; (3) generation of 3D urban models to demonstrate the model’s capability for automated urban morphology construction, with specific potential for examining urban heat island effects; (4) examination of the model’s adaptability in urban planning contexts in projecting urban morphological transformations. By integrating urban morphological inputs from eight representative Chinese metropolises, the model’s efficacy was assessed both qualitatively and quantitatively, achieving an RMSE of 0.187, an R² of 0.78, and a PSNR of 14.592. In a generalized test of urban morphology prediction through LCZ classification, exemplified by the case of Zhuhai, results indicated the model’s effectiveness in categorizing LCZ types. In conclusion, the integration of urban morphological data from eight representative Chinese metropolises further confirmed the model’s potential in climate-adaptive urban planning. The findings of this study underscore the potential of generative algorithms based on LCZ types in accurately forecasting urban morphological development, thereby making significant contributions to sustainable and climate-responsive urban planning. Full article

Bridge construction equipment (BCE) is crucial for efficiently executing large-scale infrastructure projects, particularly those involving continuous long-span bridges. Current BCE technologies, like the Overhead Movable Scaffolding System (OMSS), are often chosen for their high efficiency and cost-effective reusability. However, the lack of a standardised design framework tailored to Australian conditions complicates the design process, potentially leading to increased inefficiencies and safety concerns. This research project seeks to establish a novel design procedure for BCE, using the OMSS in Australia as a case study. The project adopts parametric design techniques using Rhinoceros (Rhino) 3D and Grasshopper to create a three-dimensional linear model. This model undergoes initial structural optimisation with Karamba3D. Subsequent advanced analyses include linear static design assessments performed in Strand7, a sophisticated finite element analysis software. The evaluation primarily utilises Australian standards to assess performance against various load types and combinations, such as permanent (dead), imposed (live), and wind loads. The structural integrity, including maximum displacement, axial forces, and bending moments, is manually verified against the analysis outcomes. The results confirm that the OMSS model adheres to ultimate and serviceability limit state requirements, affirming the effectiveness of the proposed design procedure for BCE. The research culminates in a design procedure flowchart and further suggests future research directions to refine BCE design methodologies for complex bridge construction scenarios. Full article

The construction industry in developing countries faces persistent challenges, including limited funding, poor infrastructure, insufficient use of technology, and weak quality management practices. These issues reduce productivity, compromise safety, and lower efficiency, often resulting in project delays, cost overruns, and substandard structures. This study introduces a safety and quality management framework that uses affordable camera technology and a structured web-based platform to address these challenges. The proposed system is designed to identify, document, and resolve potential issues systematically, fostering safer and more efficient construction environments. This research addresses the gap between the potential of technological advancements and their limited adoption in resource-constrained settings. Financial barriers often limit the availability of expertise on-site and restrict access to sophisticated tools, while inadequate quality control exacerbates risks, wastes resources, and undermines project outcomes. By introducing an affordable, easy-to-deploy solution, this study aims to bridge that gap and improve industry practices. Initial case studies have demonstrated promising results, including achieving acceptable quality and safety levels through the help of expertise from abroad. This paper details the design, implementation, and scalability of the proposed system while highlighting its adaptability to diverse construction contexts in developing nations. Additionally, it emphasizes the broader benefits of integrating technology into the construction industry, such as promoting economic growth and supporting sustainable development. Adopting this high-impact, cost-effective solution has the potential to significantly enhance technical capabilities, improve efficiency, and elevate social conditions through safer and more sustainable construction practices. This framework represents a transformative opportunity for the construction industry in developing countries, contributing to long-term progress and prosperity. Full article

Extracting building outlines from 3D models poses significant challenges stemming from the intricate diversity of structures and the complexity of urban scenes. Current techniques heavily rely on human expertise and involve repetitive, labor-intensive manual operations. To address these limitations, this paper presents an innovative automatic technique for accurately extracting building footprints, particularly those with gable and hip roofs, directly from 3D data. Our methodology encompasses several key steps: firstly, we construct a triangulated irregular network (TIN) to capture the intricate geometry of the buildings. Subsequently, we employ 2D indexing and counting grids for efficient data processing and utilize a sophisticated connected component labeling algorithm to precisely identify the extents of the roofs. A single seed point is manually specified to initiate the process, from which we select the triangular facets representing the outer walls of the buildings. Utilizing the projection histogram method, these facets are grouped and processed to extract regular building footprints. Extensive experiments conducted on datasets from Nanjing and Wuhan demonstrate the remarkable accuracy of our approach. With mean intersection over union (mIOU) values of 99.2% and 99.4%, respectively, and F1 scores of 94.3% and 96.7%, our method proves to be both effective and robust in mapping building footprints from 3D real-scene data. This work represents a significant advancement in automating the extraction of building footprints from complex 3D scenes, with potential applications in urban planning, disaster response, and environmental monitoring. Full article

Delays and disruptions (D&D) are considered chronic peculiarities of the construction phase of the built environment, especially in megaprojects. Systematic monitoring of claimable D&D causes becomes crucial for the contractors to compensate for their losses caused by delays and disruptions, enabling sustainable use of resources. Thus, this study proposed a delay and disruption (D&D) cause monitoring framework that enables contractors to timely and accurately detect claimable delays and disruption causes/events in megaprojects. At the outset, a systematic literature review was conducted to design an initial version of the framework and extract claimable D&D. Then, focus group discussion (FGD) sessions were conducted to revise and refine the initial version of the framework and a list of claimable D&D causes. Next, a fuzzy Analytical Hierarchy Process (AHP) analysis was conducted to determine the relative importance of each claimable D&D cause in terms of its impact on the megaprojects. Finally, a consistency analysis was conducted to demonstrate the reliability of the dataset. Findings revealed that claimable D&D causes are indispensable parts of the claim management process. In this manner, the proposed framework recommends monitoring the claimable D&D causes regularly during the whole construction phase of the megaprojects. The fuzzy AHP analysis also revealed that causes such as “Suspension of project activities by the owner”, “Errors and clashes in the design”, “Shortage of construction materials in the market”, “Discovery of fossils and historical artifacts”, “Unavailability of the commissioning team on the due date”, and “Late delivery of testing materials and utilities by the owner” were particularly rated as highly critical causes, needing urgent and sophisticated monitoring plan for timely detection and data collection. By introducing a proactive approach to avoid lengthy and costly dispute resolution processes, this study enables decision-makers to enhance sustainability in the built environment. Full article

Rock bursts are among the most severe and unpredictable hazards encountered in deep rock engineering, posing substantial threats to both construction safety and project progress. This study provides a comprehensive investigation into how moisture infiltration influences the propensity for rock bursts, aiming to establish new theoretical foundations and practical methods for their prevention. Through the analysis of meticulous laboratory mechanical experiments and sophisticated numerical simulations, we analyzed the variations in the physical and mechanical properties of rocks under different moisture conditions, with a particular focus on strength, brittleness, and energy release characteristics. The findings reveal that moisture infiltration significantly diminishes rock strength and reduces the likelihood of brittle fractures, thereby effectively mitigating the risk of rock bursts. Additionally, further research indicates that in high-moisture environments, the marked reduction in rock burst tendency is attributed to increased rock toughness and the suppression of crack propagation. This study advocates for the implementation of moisture control measures as a pre-treatment strategy for deep rock masses. This innovative approach presents a viable and effective solution to enhance engineering safety and improve construction efficiency, offering a practical method for managing rock burst risks in challenging environments. Full article

In the ambitiously evolving construction industry of Kuwait, characterised by its vision 2035 and rapid technological integration, there exists a pressing need for advanced analytical frameworks. The pressing need for advanced analytical frameworks in the Kuwait Construction Market arises from the necessity to identify inefficiencies, predict market trends, and enhance decision-making processes. For instance, these frameworks can be used to detect anomalies in investment patterns, forecast the impact of economic changes on project timelines, and optimise resource allocation by analysing labour and material supply data. By leveraging deep learning techniques, such as autoencoder neural networks, stakeholders can gain deeper insights into the market’s complexities and improve strategic planning and operational efficiency. This research paper introduces a deep learning approach utilising an autoencoder neural network to analyse the complexities of the Kuwait Construction Market and identify data irregularities. The construction sector’s significant investment influx and project expansion make it an ideal candidate for deploying sophisticated analytical techniques to detect anomalous patterns indicating inefficiencies or unveiling potential opportunities. Our approach leverages the capabilities of autoencoder architectures to delve into and understand the prevalent patterns in market behaviours. This analysis involves training the autoencoder on historical market data to learn the normal patterns and subsequently using it to identify deviations from these learned patterns. This allows for the detection of anomalies that may lead to operational or financial consequences. We elucidate the mathematical foundations of autoencoders, highlighting their proficiency in managing the complex, multidimensional data typical of the construction industry. Through training on an extensive dataset—comprising variables like market sizes, investment distributions, and project completions—our model demonstrates its ability to pinpoint subtle yet significant anomalies. The outcomes of this study enhance our understanding of deep learning’s pivotal role in construction and building management. Empirically, the model detected anomalies in transaction volumes of lands and houses, highlighting unusual spikes that correlate with specific market activities. These findings demonstrate the autoencoder’s effectiveness in anomaly detection, emphasising its importance in enhancing operational efficiency and strategic planning in the construction industry. Full article

In response to the critical need for enhanced resource management in the construction industry, this research develops an innovative, integrated methodology that synergistically combines Agent-Based Modeling (ABM), Building Information Modeling (BIM), and Bluetooth Low Energy (BLE) technologies. Central to our approach is a sophisticated technological framework that incorporates a Client Early Warning System (CEWS) and a Decision Support System (DSS). These systems facilitate real-time monitoring and management of construction resources, ensuring operational efficiency and optimal resource utilization. Our methodology was empirically validated through a comprehensive case study at Helwan University’s College of Engineering. The results demonstrated a significant enhancement in operational efficiency, particularly in resource allocation and progress tracking. Key practical outcomes include the development of a CEWS master dashboard that provides in-depth, real-time insights into project metrics. This dashboard was crucial for managing compliance with health protocols during the COVID-19 pandemic, showcasing the framework’s adaptability to critical health standards. Further, the integration of indoor tracking technology revolutionized attendance tracking by replacing outdated manual methods with automated processes. This capability not only underscores the practical applicability of our research but also establishes a new benchmark for future technological advancements in construction project management. Our study sets the stage for subsequent innovations, paving the way for a more connected, efficient, and data-driven approach in the construction industry. Full article

Sustainability and resilience in addressing construction’s environmental, social, and economic challenges rely on interoperability. A model-centred approach using standardised information structures like industry foundation classes (IFC) is essential for data sharing in architecture, engineering, construction, and facility management. Achieving complete interoperability across domains requires further research. This review paper focuses on IFC schema, highlighting upcoming developments like IFC 5 and “IFC x”, with a core emphasis on modularisation to enhance domain interoperability, improved links between building information modelling (BIM) and geographic information systems (GIS), along with IoT integration into BIM, cloud-based collaboration, and support for other advanced technologies such as augmented reality (AR), virtual reality (VR), artificial intelligence (AI), and digital twins. Through a critical examination of the IFC and an outlook towards its future enhancements, the research has the potential to offer valuable insights into shaping the trajectory of future advancements within the AEC and facility management sectors. The study’s discoveries could aid in establishing standardised data exchange protocols in these industries, promoting uniformity across projects, facilitating smoother communication, and mitigating errors and inefficiencies. Anticipating enhancements in the IFC could catalyse innovation, fostering the adoption of emerging technologies and methodologies. Consequently, this could drive the creation of more sophisticated tools and procedures, ultimately enhancing project outcomes and operational effectiveness. Full article