In the transition toward a more digital and data-driven construction industry, understanding how Artificial Intelligence (AI) and Building Information Modeling (BIM) are integrated is key to planning, delivering, and operating projects effectively. This review examines recent studies to identify usage patterns of AI and BIM. Searches were conducted on the Web of Science Core Collection from 2022 to 2025. After running a reproducible review protocol aligned with PRISMA 2020, which began with 1212 articles, and after a funneling process, 12 studies met the predefined eligibility criteria. In the present study, the synthesis was non-meta-analytic; instead, the information was analyzed by using standardized tabulation with a consistent format and compared using a two-level weighting scheme. The methodological approach combines full-text reading and descriptive coding with a reproducible weighting scheme that accounts for mentions per study and integrates them at the corpus level using open-source tools. The results show a strong focus on Deep Learning (DL), with a greater presence in Digital Twins (DT) and BIM Modeling (BIMM); Multidimensional BIM (4D/5D) appears as a secondary line, while the Common Data Environment (CDE) and Clash detection (CD) are sporadic. The coupling of DL-DT and DL-BIMM predominates. Simultaneously, Machine Learning (ML) provides explainable analysis on structured data, and Generative Adversarial Networks (GAN) and Automated Machine Learning (AutoML) with Machine Learning Operations (MLOps) act as enablers for data generation/adaptation and deployment with traceability. It is concluded that advancing metrics and shared datasets, especially for CDE and CD, along with developing reproducible workflows oriented toward MLOps, are key to scaling AI in real-world environments.

Organizational culture is vital for adopting Lean Construction (LC) methodologies in the construction industry. LC maximizes customer value by minimizing waste and optimizing processes. Implementing this philosophy necessitates a shift in values, attitudes, and behaviors. A culture promoting collaboration, innovation, and continuous improvement aids the acceptance of Lean principles, enhancing efficiency and sustainability. Effective leadership and employee engagement are essential to tackle barriers, like resistance to change and lack of strategic alignment. Organizational culture aligns individual and organizational goals, fostering open communication and continuous learning. This study shows how organizations amplify LC benefits by integrating its principles with a culture of adaptability, shared responsibility, and operational excellence, boosting productivity and positioning companies as leaders in a competitive and innovation-driven sector.

Modern Architecture, Engineering, and Construction (AEC) projects require integrated approaches for efficiency and sustainability. Lean Construction (LC) optimizes processes and minimizes waste, but cultural resistance and limited stakeholder engagement hinder its adoption. Enterprise Architecture (EA) aligns strategies with technological and operational processes, enhancing decision-making and resource use. This study examines the combined impact of LC and EA on project outcomes, utilizing practices, developing strategies, and validating findings through a focus group at a large engineering firm. Results indicate that integrating these frameworks improves project coordination, reduces inefficiencies, and fosters continuous improvement, despite challenges like resistance to change and training gaps. The study highlights interdisciplinary approaches that unite technology, processes, and people. Combining LC with a robust EA framework enhances collaboration, agility, and alignment, promoting sustainable innovation in the AEC sector.

Early-stage regulatory decisions play a critical role in the delivery of construction projects. However, regulatory processes are frequently characterized by fragmented information, limited transparency, and iterative rework, generating significant waste that contradicts Lean Construction principles. From a Lean perspective, regulatory …

Despite the importance of adaptive reuse for urban renewal, municipal records of changes in property destinations remain an untapped resource for understanding regulatory barriers. This study develops an interpretable machine learning (ML) framework to screen historical administrative non-approval using 42,350 municipal records from a mid-sized city in southern Chile spanning 2010 to 2025. Historical memory features were constructed exclusively from outcomes available before each application, thereby preventing temporal leakage. Records from 2010–2022 were used for training, 2023 was reserved for validation, and 2024–2025 constituted a locked test set. A logistic regression model combining auditable risk flags derived from GIRO (declared economic or functional activity) with historical property, address, and activity category information was selected by maximizing the F1-score (the harmonic mean of precision and recall) subject to a recall ≥ 0.85. At the validation-selected threshold of 0.285, the model achieved a receiver operating characteristic area under the curve (ROC-AUC) of 0.778, a precision–recall area under the curve (PR-AUC) of 0.720, an F1-score of 0.719, recall of 0.887, and precision of 0.604. The model flagged 66.8% of applications for prioritized review. The proposed framework supports human-in-the-loop screening and institutional learning; it neither determines regulatory compliance nor automates municipal decisions.