Kareem Adel

Budget overruns driven by unpredictable building material prices are a persistent challenge in emerging economies, yet reliable price forecasting tools for structural materials such as steel reinforcement bar (RFT) remain largely unavailable to building project teams. This study develops an Autoregressive Distributed-Lag (ARDL) model to forecast RFT prices in Egypt using macroeconomic leading indicators. A structured filtering pipeline—stationarity testing, Variance Inflation Factor (VIF) screening, and Granger causality testing—reduced 22 candidate variables to nine predictors: producer price index (PPI), loan rate (LR), discount rate (DR), Egyptian Stock Exchange Index (EGX30), money supply (M0), exchange rate (ER), iron ore prices (ORE), American stock index (S&P500), and Hang Seng Index (HSI). ARDL bounds testing confirmed cointegration across look-back periods (LBP) of 3, 6, and 9 months. Comprehensive diagnostics confirmed model stability, homoscedasticity, and the absence of serial correlation. The ARDL was benchmarked against an Ordinary Least Squares (OLS) baseline and a first-difference vector autoregression (VAR) model. Evaluated as genuine multi-step (dynamic) forecasts over the held-out crisis-period window, the 3-month model achieved an out-of-sample mean absolute error of approximately 5.9% in price terms, retaining a clear advantage over the OLS baseline at that horizon. The proposed framework provides quantity surveyors, cost consultants, and project managers with a practical tool for setting RFT budgets at the tendering stage and optimising procurement timing. It is transferable to other building materials (cement, structural steel sections, glazing) across emerging-market construction economies.

Purpose: The adoption of building information modeling (BIM) is accelerating globally, accompanied by a proliferation of BIM protocols. These protocols facilitate structured collaboration, standardized information management and interoperability among stakeholders in the architecture, engineering and construction (AEC) industry. Despite their growing importance, BIM implementation faces challenges that hinder successful integration. The purpose of this research is to develop a framework that utilizes Natural Language Processing (NLP) techniques and recommendation systems to analyze and address critical challenges in BIM protocols. Design/methodology/approach: This research proposes a framework that uses natural language processing (NLP) techniques and recommendation systems to examine critical BIM challenges across three widely used protocols: the Ohio BIM Protocol (2010), the AEC (UK) BIM Protocol (2012) Version 2.0 and the Construction Industry Council BIM Protocol Second Edition (2018). The framework comprises three modules: the BIM Challenges Identification Module, the Challenge Similarity Module and the Tailored Recommendation Module. The Challenge Similarity Module applies term frequency–inverse document frequency with three similarity measures: cosine similarity, Euclidean distance and Manhattan distance to assess the alignment of challenges with protocols. Findings: Results show that while each method provides distinct insights, they consistently identify specific protocols as more closely related to certain challenges, demonstrating the robustness of the approach. The Tailored Recommendation Module introduces an innovative recommendation system that leverages similarity to match user-defined preferences such as data security, collaboration or adherence to BIM standards with protocol features. This ensures that stakeholders receive a customized, data-driven recommendation aligned with project needs. Originality/value: This study contributes to both theory and practice by providing a systematic, preference-driven approach to BIM protocol selection, thereby enhancing the efficiency and effectiveness of BIM adoption.

This paper presents a scientometric review that provides a quantitative perspective on the evolution of Automation in Construction Journal (AICJ) research, emphasizing its developmental paths and emerging trends. The study aims to analyze the journal’s growth and citation impact over time. It also seeks to identify the most influential publications and the cooperation patterns among key contributors. Furthermore, the study explores the journal’s primary research themes and their evolution. Accordingly, 4084 articles were identified using the Web of Science (WoS) database and subjected to a multistep analysis using VOsviewer version 1.6.18 and Biblioshiny as software tools. First, the growth and citation of the publications over time are inspected and evaluated, in addition to ranking the most influential documents. Second, the co-authorship analysis method is applied to visualize the cooperation patterns between countries, organizations, and authors. Finally, the publications are analyzed using keyword co-occurrence and keyword thematic evolution analyses, revealing five major research clusters: (i) foundational optimization, (ii) deep learning and computer vision, (iii) building information modeling, (iv) 3D printing and robotics, and (v) machine learning. Additionally, the analysis reveals significant growth in publications (54.5%) and citations (78.0%) from 2018 to 2023, indicating the journal’s increasing global influence. This period also highlights the accelerated adoption of digitalization (e.g., BIM, computational design), increased integration of AI and machine learning for automation and predictive analytics, and rapid growth of robotics and 3D printing, driving sustainable and innovative construction practices. The paper’s findings can help readers and researchers gain a thorough understanding of the AICJ’s published work, aid research groups in planning and optimizing their research efforts, and inform editorial boards on the most promising areas in the existing body of knowledge for further investigation and development.

Integrating Artificial Intelligence (AI) with Blockchain Technology (BT) is deemed the fourth generation of BT applications (Blockchain 4.0). This generation has gained considerable attention from the research community. Such attention has led to a vast amount of scientific literature. However, a comprehensive quantitative analysis of this literature is still missing. The present study conducts a scientometric analysis to explore and characterize the development track and trends of BT-AI research. Using the Web of Science (WoS) Core Collection database, a total of 2615 peer-reviewed journal articles were identified between 2017-2023 and extracted for analysis, while employing VOSviewer and Biblioshiny as software tools. First, the publication trend was analyzed, and the pivotal articles were identified. Second, the scientific collaboration networks were analyzed and mapped to identify the key researchers, countries, and organizations. Third, the sources’ productivity and citation were analyzed and mapped to identify the dependable sources of information and the best-fit sources for publishing the BT-AI studies. Fourth, the conceptual structure for the BT-AI literature was analyzed and visualized using keywords co-occurrence and keywords thematic evolution to explore and identify the research hotspots and emerging themes. The findings of this study can help in further familiarizing new researchers with BT-AI literature and assist practitioners, policy-makers, and editors to focus on the promising and arising BT-AI trends for further development.

Construction projects’ performance is not self-regulating. Therefore, a continuous progress tracking and monitoring process is highly demanded to avoid potential deviations or misalignments. The current practice for the progress tracking and monitoring process suffers from heavily intermediated workflows, human errors, transfer latencies, inaccuracies, and/or information holes. Such issues could gradually lead to severe delays or even complete project failure. This research introduces a novel Peer-to-Peer (P2P) system that relies on Blockchain Technology (BT) and Inter-Planetary File System (IPFS) for managing progress information and as-built digital assets or files. The system is developed based on a three-step approach. First, two chaincodes are formulated for mapping and governing the data operations. Second, a private blockchain network is configured based on Hyperledger Fabric as a hosting platform, including the relevant stakeholders. Third, a private IPFS network is configured and coupled with a cluster service to manage and distribute the off-chain visuals and as-built digital assets. A case study for a non-residential construction project is utilized to test and verify the system’s practicability and assess its performance. The research significance is anticipated in diverse practical areas, including but not limited to; boosting coordination and trust among stakeholders, tracing progressive elaboration of As-built digital assets, accelerating incremental payments processing, assessing overall project performance and on-site productivity, supporting delay analysis and claim/dispute management, and streamlining data flow between the construction phase and the operation and maintenance phase. Further, the system’s future is mapped by evolving it as a sub-unit in a more advanced data model.

Infrastructural assets such as roads, bridges, and buildings make a considerable contribution to national economies. These assets deteriorate due to aging, environmental conditions, and other external factors. Maintaining the performance of an asset in line with rational repair strategies represents a considerable challenge for decision-makers, who may not pay attention to developing adequate maintenance plans or leave the assets unmaintained. Worldwide, organizations are under pressure to ensure the sustainability of their assets. Such organizations may burden their treasury with random maintenance operations, especially with a limited budget. This research aims to develop a generalized condition assessment approach to monitor and evaluate existing facility elements. The proposed approach represents a methodology to determine the element condition index (CI). The methodology is reinforced with an artificial neural network (ANN) model to predict the element deterioration. The performance of this model was evaluated by comparing the obtained predicted CIs with ordinary least squares (OLS) regression model results to choose the most accurate prediction technique. A case study was applied to a group of wooden doors. The ANN model showed reliable results with R² values of 0.99, 0.98, and 0.99 for training, cross-validation, and testing sets, respectively. In contrast, the OLS model R² value was 1.00. These results show the high prediction capability of both models with an advantage to the OLS model. Applying this approach to different elements can help decision-makers develop a preventive maintenance schedule and provide the necessary funds.

Integrating Artificial Intelligence (AI) with Blockchain Technology (BT) is deemed the fourth generation of BT applications (Blockchain 4.0). This generation has gained considerable attention from the research community. Such attention has led to a vast amount of scientific literature. However, a comprehensive quantitative analysis of this literature is still missing. The present study conducts a scientometric analysis to explore and characterize the development track and trends of BT-AI research. Using the Web of Science (WoS) Core Collection database, a total of 956 peer-reviewed journal articles were identified between 2016-2021 and extracted for analysis, while employing VOSviewer, CiteSpace, and Biblioshiny as software tools. First, the publication trend was analyzed, and the pivotal articles were identified. Second, the scientific collaboration networks were analyzed and mapped to identify the key researchers, countries, and organizations. Third, the sources’ productivity and citation were analyzed and mapped to identify the dependable sources of information and the best-fit sources for publishing the BT-AI studies. Fourth, the conceptual and intellectual structures for the BT-AI literature were analyzed and visualized using keywords co-occurrence, keywords thematic evolution, and reference co-citation analyses to explore and identify the research hotspots and emerging themes. The findings of this study can help in further familiarizing new researchers with BT-AI literature and assist practitioners, policy-makers, and editors to focus on the promising and arising BT-AI trends for further development.