International Journal of Building Information Modeling Applications in Construction (e-3107-8710)

Artificial Intelligence in Construction: Applications, Benefits, and Adoption Challenges

Mian Md Jawad Ibne Iqbal, Ar. Sazzadur Rasheed — Tue, 21 Apr 2026 00:00:00 +0000

Artificial intelligence (AI) has emerged as a transformative technology with significant potential to address long-standing challenges in the construction industry, including low productivity, safety risks, cost overruns, and inefficiencies. This study presents a structured and critical review of AI applications in construction, synthesizing existing literature to examine key technologies, practical implementations, benefits, challenges, and future research directions. A structured narrative review approach was adopted, drawing on peer-reviewed studies sourced from major academic databases such as Scopus, Web of Science, and ScienceDirect. The findings indicate that AI technologies—including machine learning, computer vision, robotics, and their integration with building information modelling (BIM), internet of things (IoT), and digital twins—are increasingly applied across various stages of the construction lifecycle. These applications demonstrate considerable potential to enhance safety performance, improve productivity, optimize resource utilization, strengthen quality control, and support sustainable construction practices. However, the review also reveals that AI adoption remains fragmented and limited in practice, with many applications confined to experimental or pilot stages. Key challenges identified include data fragmentation, lack of standardization, high implementation costs, limited technical expertise, organizational resistance, and regulatory uncertainty. The study highlights that these barriers are interconnected and extend beyond technological constraints, reflecting the complex socio-technical nature of the construction industry. This study contributes to the literature by providing a comprehensive and critically synthesized understanding of AI in construction, identifying key research gaps, and proposing future directions. These include the development of standardized data frameworks, large-scale empirical validation, integration of AI with emerging digital technologies, and advancement of explainable and trustworthy AI systems. The findings offer valuable insights for researchers, practitioners, and policymakers seeking to facilitate effective and responsible AI adoption in the construction sector.

Integration of BIM for Simulated Real-time Management of Structural Material Waste in Residential Construction: A Prototype Framework

Tirona Jayashree, Nagaraju Kaja — Mon, 20 Apr 2026 00:00:00 +0000

Introduction: Rapid urban growth and the increasing demand for high-rise residential buildings in India have led to a significant rise in the use of structural materials and the generation of construction waste. Materials such as concrete, steel, and masonry contribute significantly to this waste; however, most current management practices are reactive and often do not allow for real-time monitoring of materials used during construction. The absence of real-time tracking systems contributes to material inefficiencies, cost overruns, and environmental concerns within high-rise residential projects.

Purpose: The purpose of the study is to connect design-stage planning with on-site construction monitoring, creating a workflow that supports resource efficiency and encourages circular economy practices.

Aim and Objectives: This research aims to explore the application of BIM for simulated structural material waste management in high-rise residential projects, using a prototype framework. The study will include examining current material waste monitoring procedures, creating a BIM model including parameters related to material use, using this BIM model for automated material quantification and clash detection, and implementing a tagging system to identify reusable leftover materials.

Methodology: A mixed-method approach was adopted, which included literature review, developing a BIM model utilizing Autodesk Revit, running a clash detection analysis through Navisworks, scheduling automatic quantities and near real-time tracking (simulation-based) based on the delivered, used and reusable material quantity.

Results: The findings indicate that utilizing early clash detection has the potential to eliminate reworking on site, whilst utilizing continuous material tracking not only enhances visibility but also allows identifying materials that can be reused. In addition to supporting better plans for procurement, this method will also help to reduce the amount of waste produced on construction sites unnecessarily.

Discussion: This research discussion demonstrated that integrating BIM-based clash detection, simulated material tracking, and reuse tagging has the potential to contribute to structural material waste reduction and improved construction resource efficiency in residential projects, as illustrated through a prototype G+7 case study.

Life Cycle Assessment of Building Facade Material in Construction Industry: A Study on Residential Buildings in Pakistan

Mehtab M. Ahsan, Ping Cao, Muhammad Muzammil, LI Juan — Fri, 16 Jan 2026 00:00:00 +0000

The high rate of urbanization, high general expenditure on housing and cooling with shortages of water, power and a large population exceeding 240 million, make the residential sector of Pakistan a great challenge to the environment since most of the energy used is on construction and operation of houses. Through a cradle-to-grave evaluation that utilizes BIM for over 50 years, it compares traditional materials (common brick, cement plaster, single glazes) with the sustainable ones (lightweight concrete blocks, lightweight plaster, polyurethane insulation, triple-glazed windows). The present case will result in embodied CO₂ emissions reduction to 48.9% (56.44 tonnes to 28.83 tonnes) and maximum operational reduction to 17.5%. The switching towards lightweight concrete blocks (W1), lightweight plaster (P1), polyurethane insulation (WI1), and triple-glazed windows, in particular, cut the peak cooling total load (PCTL) and CO₂ by 15.8%, 7.86%, 9.5%, and 17.5%, respectively. These are the locally sourced, inexpensive materials that increase the energy efficiency of the hot and dry climate of Multan, where cooling takes up the majority of the energy demand. These results are scalable and cost-effective to the residential sector and directly contribute to the Nationally Determined Contributions of a 50% reduction in GHG by 2030 in Pakistan and Sustainable Development Goals 11 (Sustainable Cities) and 13 (Climate Action).

Robotic Automation of Bricklaying, Plastering, and Wall Painting to Overcome Manual Limitations in Modern Construction

Rutuja P. Salunke, Monika B. Kanase, M. S. Shikalgar — Tue, 09 Dec 2025 00:00:00 +0000

This project presents a detailed comparative study between the SAM100 robotic bricklaying system and traditional manual masonry. The construction industry continues to face challenges such as labour shortages, low productivity, safety risks and material wastage. Robotic systems like SAM100 offer automated bricklaying with higher productivity and reduced labour dependency. The study evaluates both methods based on time, labour requirement, cost, mortar wastage, productivity rate, and overall project efficiency. A simulated calculation for a 30 m² wall shows that SAM100 reduces labour costs by nearly 30% of total cost along with reduced mortar waste and construction time. The results demonstrate that robotic masonry significantly improves efficiency and can be beneficial for large-scale construction projects. The study also highlights limitations of SAM100 in complex areas, setup costs, and the need for trained operators.

From Steel Skeletons to Glass Icons: The Modern Skyscraper

Bharath A., Mahadeva M. — Fri, 28 Nov 2025 00:00:00 +0000

This research gives a detailed account of skyscrapers and mega-tall buildings, charting their evolution from foundational 19th-century technological achievements-specifically steel-frame construction and the establishment of safe elevators-to their complex, contemporary urban function. The core themes center on the resolution of significant engineering challenges in design and construction, such as structural complexities and sophisticated systems for mitigating lateral loads arising from wind and seismic activity, together with ensuring robust fire safety and efficient vertical transportation systems. From a financial standpoint, architectural innovations such as offset cores and the imperative for maximizing space efficiency to ensure high financial returns on expensive urban land are crucial; the paper includes specific studies detailing core arrangements, structural systems, and average efficiency metrics in North American towers. Second, the research establishes a key link between tall buildings and urban sustainability: well-planned high-rise buildings can limit sprawl and conserve natural resources. On the other hand, the research into broader socio-economic effects found that the general development of tall buildings is a strong correlate for the well-being and size of a city, though mega-tall projects represent those that require significant economic justification owing to their need for highly innovative and expensive design solutions.

A Fingerprint Matrix Methodology for the Predictive Diagnosis of Incipient Wall Failure: A Mathematical Modeling and Machine Learning Approach

Belay Sitotaw Goshu — Thu, 13 Nov 2025 00:00:00 +0000

This study presents an integrated framework for structural health monitoring (SHM) of concrete infrastructures, combining multimodal sensor networks, finite element analysis (FEA), and deep learning (DL) to detect, localize, and prognose damage under coupled hydromechanical loads. A hybrid sensor array at 40% density achieved 92% coverage on a 7×5 m simulated panel, capturing raw fingerprints, strain (–0.046 to 1.869), moisture (–0.032 to 0.899), and acoustic (–0.161 to 3.595), with interpolation reducing noise by 28% and revealing temporal lags (τ=0.8 steps) and correlations (r = 0.13–0.36). FEA delineated damage hotspots (max 0.88), stress peaks (80 MPa), strain amplifications (0.0225), and moisture-displacement synergies (r = 0.84), compressing data via PCA (85% variance in PC1). ResNet-18 DL models on 5000 samples yielded failure classification accuracy 0.95–0.98 (AUC = 0.94, F1 = 0.589 binary), location prediction 0.80 (macro F1 = 0.81), with confusion matrices exposing hybrid biases (5% FP) and ROC/PR curves optimizing thresholds (AP = 0.59). Temporal confidences forecasted precursors (τ = 10 steps, r = 0.78), distributions matched actuals (KL = 0.15), and location under variability correlated with loads (r = 0.45, ANOVA F = 4.2). The pipeline bridges simulation-reality (RMSE = 0.07), enabling probabilistic alerts (P_f < 10^-3) and edge deployment (15 ms inference), potentially extending asset life 20% with 12:1 ROI amid climate stressors. This advances SHM from reactive to predictive, fostering resilient designs.

Experimental and FEA-based Performance Evaluation of Geopolymer Versus OPC Concrete

Md. Shaon Ahmed, Md. Asaduzzaman, M. Moniruzzaman, M. T. Islam — Mon, 06 Oct 2025 00:00:00 +0000

The rising environmental concerns associated with ordinary Portland cement (OPC) have accelerated the development of sustainable binders such as geopolymer concrete (GPC), produced from industrial by-products like ground granulated blast furnace slag (GGBS). This study presents a combined experimental and numerical investigation of GPC, focusing on its mechanical and structural performance. Laboratory testing on cylindrical, cubic, and beam specimens provided compressive, tensile, and flexural strength data, which were processed in MATLAB to derive input parameters for nonlinear finite element analysis (FEA) in ABAQUS. The numerical model, developed using the concrete damaged plasticity (CDP) approach, incorporated experimentally validated stress–strain relationships. Results revealed that GPC outperformed OPC, exhibiting 14.6% higher compressive strength and 17.5% lower deflection, with von Mises stress and load-deflection analyses confirming enhanced load-bearing capacity and reduced crack propagation. The simulation outcomes closely matched experimental data, maintaining discrepancies within 10%. Overall, the integration of MATLAB and ABAQUS proved effective for predicting GPC performance, reinforcing its potential as a sustainable alternative to OPC in structural applications.

A Comparison between Cement and Crushed Concrete in Improving the Strength of Soft Soils: A Review

Sarah Yassin Khdear — Thu, 18 Sep 2025 00:00:00 +0000

Civil engineering projects, including buildings, bridges, retaining walls, etc., require soil with an adequate bearing capacity to support the heavy loads exerted by these structures within the allowable settlement limits. Practically, not all soils provide sufficient bearing capacity, and it is required to spend much time, effort, and cost to prepare such soils for different projects. Soft clayey soils are repeatedly encountered in civil engineering projects, and improving their capacity is essential. This improvement can be achieved by mixing some materials with soft soils to improve their behavior, like Portland cement, lime, fly ash, sand, etc. The high cost and the high depletion of natural resources associated with the manufacture of such mixing materials made it necessary to use some recycled materials instead. Moreover, the extraction of natural resources releases carbon dioxide (CO₂) emissions and consequently causes pollution. Construction and demolition wastes form a large quantity of overall waste materials. Crushed concrete resulting from the demolition of old buildings or concrete barriers is widely available and can be used as recycled mixing material to enhance soil properties. This research reviews some research papers investigating the effects of recycled crushed concrete, as compared with Portland cement, on the compaction characteristics, compressive strength, and California-bearing-ratio (CBR) of soft soils.

A Comparative Study of Self-Compacting Concrete and Self-Healing Concrete Using the Same Mix Ratios

Mohammed Ali Abdulrehman, Akram Q Moften, Laith Emad — Sat, 31 May 2025 00:00:00 +0000

This work looked into how two types of concrete Self-compacting (SCC) and self-healing (SHC) respond when traditional plasticizers get swapped out for Bacillus bacteria, all using the same mixing recipe. The study is not just about the usual metrics but also dives into compressive and tensile strength alongside a sort of “self-repair” ability. Generally speaking, both concrete mixes performed pretty well in everyday mechanical tests, but the self-healing mix really shines over time. In most cases, when moisture enters the picture, those bacteria trigger a reaction that forms calcite, patching tiny cracks and boosting overall durability even if the repair process sometimes shows up as a bit slower than expected. Interestingly, while SHC’s compressive strength was a touch lower than that of SCC, the trade-off seems fair given its enhanced ability to mend itself over time. It is like, here we have a material that may start a smidge weaker, but then steadily builds up its resilience. This also hints at the bigger picture biotech tweaks in concrete mixtures might not only secure our buildings but could also become a smart choice in environments like healthcare, where reducing maintenance and keeping things safe are huge priorities. All in all, mixing in these bio-based elements could open doors to innovative building designs that better handle environmental stress, pushing us toward longer-lasting construction and even improved infrastructure in places that matter most.

Seismic Resilience of Beam-to-Beam Connected RC Buildings: A Comparative Study with Conventional Frames

Md. Sohel Rana, Abu Sufian Md. Zia Hasan, Md. Kamruzzaman — Fri, 30 May 2025 00:00:00 +0000

This study presents a comprehensive comparative analysis of the seismic performance between conventional Reinforced Concrete (RC) moment frames and innovative beam-to-beam connected RC structural systems. With increasing architectural complexity in modern construction, alternative connection methodologies are being explored for their potential advantages in design flexibility and construction efficiency. The research evaluates two prototype buildings a 3-span and 4-span structure designed according to BNBC 2020 provisions for seismic Zone 2 (Z=0.12) in Rajshahi, Bangladesh. Both structural systems were analyzed using equivalent static analysis methods to assess key seismic performance parameters including inter-story drift ratios, lateral stiffness characteristics, displacement profiles, overturning moments, and story shear distributions. The conventional RC frame with standard beam-column connections serves as the baseline model, while the alternative system incorporates direct beam-to-beam connections at specific locations. Material properties were maintained consistent across both systems, featuring 3000 psi concrete for beams/slabs, 3500 psi for columns, and Grade 60 reinforcement. Results indicate that the beam-to-beam connected system exhibits 4–12% higher inter-story drifts and marginally increased displacement demands (1.5–2.1%) compared to conventional frames, suggesting reduced lateral stiffness. However, critical load-resisting mechanisms including overturning moments and story shear capacities showed negligible variations (<0.5%), indicating comparable strength characteristics. These findings provide valuable insights for structural engineers considering alternative connection configurations in moderate seismic regions, highlighting both the potential benefits and necessary design considerations for implementing beam-to-beam connections in RC frame structures.

Causes and Effects of Delay in Building Construction: An Assessment on Organizational Building of Lekbeshi Municipality, Surkhet

Roshani Sahi, Trilok Chandra Bista, Govinda Khatri, Mukesh Kafle — Fri, 23 May 2025 00:00:00 +0000

Construction delays remain a major challenge, impacting cost, schedule, and project success. This study examined the status of ongoing building projects in Lekbeshi Municipality, identified key delay factors, and proposed mitigation strategies using a mixed-method approach including document analysis, key informant interviews, and statistical tools like Earned Value Analysis (EVA). Findings showed significant delays, with EVA revealing a Schedule Performance Index (SPI) of 0.89, indicating schedule overruns. Delay factors were grouped into labor/resource issues, coordination and political interference, and safety/environmental concerns. Stakeholder analysis highlighted varying concerns among clients, consultants, and contractors. The study recommends targeted interventions such as better procurement, monitoring, legal frameworks, and contractor training. Statistical tests like ANOVA confirmed differing stakeholder views, suggesting tailored solutions were needed. The study emphasizes a multi-stakeholder approach and calls for future research into predictive models, digital tools, and policy reforms to improve public construction efficiency.

Challenges in Integrating Artificial Intelligence with Building Information Modeling in Construction Management: A Comprehensive Analysis

Aryansh Gupta — Thu, 15 May 2025 00:00:00 +0000

The integration of Artificial Intelligence (AI) with Building Information Modeling (BIM) presents transformative potential for enhancing efficiency, accuracy, and decision-making in construction management. However, the convergence of these technologies is met with significant challenges that hinder its widespread adoption. This paper provides a comprehensive analysis of the barriers to successful AI-BIM integration within the construction industry, focusing on technical, organizational, and data-related issues. Key challenges include interoperability between AI algorithms and BIM platforms, data privacy and security concerns, lack of standardization, high implementation costs, and the need for specialized skills and knowledge. Through a review of existing literature and expert insights, this paper highlights critical areas for improvement and proposes strategies to overcome these obstacles, ultimately aiming to foster a more seamless integration of AI within BIM for optimized construction management practices.

Investigating the Challenges of Implementing Building Information Modeling (BIM) Technology: A Case Study of Construction Sites in Birjand City, South Khorasan Center, Iran

Morteza Shirvani, Amirreza Sadeghi, Abbasali Sadeghi, Melika Mahmoodabadi — Thu, 15 May 2025 00:00:00 +0000

Today, knowledge and information are the most important variables for the comprehensive growth of economic organizations and are considered valuable and reliable resources of particular importance. The execution of operations in large-scale civil projects is classified as knowledge-based activities. Inevitably, the implementation of such projects leads to the emergence of new knowledge in technical and engineering issues and the strategic management of successful projects. Building information modeling (BIM) is not merely a technology; rather, it is a system that also encompasses other aspects of a large project, such as stakeholder relationships and project delivery processes. Furthermore, BIM is a collaborative method among project stakeholders, utilizing modern technologies to facilitate design and construction processes. In this research, the challenges of implementing BIM technology in construction sites in Birjand City, South Khorasan Center, Iran are evaluated statistically. The research indicates that equipping seasoned professionals with specialized BIM training, promoting effective communication, and providing leadership support are effective strategies for facilitating organizational change. Further investigation is required to comprehend how demographic factors influence BIM implementation.