Developing a reliable parametric cost model at the conceptual stage of the project is crucial for project managers and decision makers. Existing methods, such as probabilistic and statistical algorithms have been developed for project cost prediction. However, these methods are unable to produce accurate results for conceptual cost prediction due to small and unstable data samples. Artificial intelligence (AI) and machine learning (ML) algorithms include numerous models and algorithms for supervised regression applications. Therefore, a comparative analysis for AI models is required to guide practitioners to the appropriate model. The article focuses on investigating 20 AI techniques which are conducted for conceptual cost modeling, such as fuzzy logic model, artificial neural networks, multiple regression analysis, case-based reasoning, hybrid models, such as genetic fuzzy model, and ensemble methods such as scalable boosting trees (XGBoost) and random forest. Field canals improvement projects (FCIPs) are used as an actual case study to analyze the performance of the applied ML models. Out of 20 AI techniques, the results show that the most accurate and suitable method is XGBoost with 9.091% and 0.929 based on mean absolute percentage error and adjusted R2, respectively. Nonlinear adaptability, handling missing values and outliers, model interpretation, and uncertainty have been discussed for the 20 developed AI models. In addition, this study presents a publicly open dataset for FCIPs to be used for future models’ validation and analysis.

This systematic literature review explores the intersection of AI-driven digital twins and IoT in creating a sustainable building environment. A comprehensive analysis of 125 papers focuses on four major themes. First, digital twins are examined in construction, facility management, and their role in fostering sustainability and smart cities. The integration of IoT and AI with digital twins and energy optimization for zero-energy buildings is discussed. Second, the application of AI and automation in manufacturing, particularly in Industry 4.0 and cyber-physical systems, is evaluated. Third, emerging technologies in urban development, including blockchain, cybersecurity, and EEG-driven systems for sustainable buildings, are highlighted. The study underscores the role of data-driven approaches in flood resilience and urban digital ecosystems. This review contributes to sustainability by identifying how digital technologies and AI can optimize energy use and enhance resilience in both urban and industrial contexts.

This study introduces a pioneering structural equation modeling (SEM)-based framework to assess BIM-DT adoption readiness in sustainable construction. The research’s approach delves into the intricate correlation between key success factors (KSFs) and sustainable parameters (SPs), fostering BIM-DT deployment. This interdisciplinary perspective provides a holistic understanding of the impact of KSFs on BIM-DT adoption. The research aims to identify KSFs and parameters, prioritize their relative weights for BIM-DT implementation, analyze KSFs and SPs correlations, and offer practical insights into the findings. The results revealed the importance of sustainability considerations in BIM-DT adoption, with cost optimization and resource management playing pivotal roles. The findings also revealed that the readiness of the organization to adopt the technology, availability of technology operators, availability of standards and codes for the new technology, availability of knowledge, skills, and competencies to adopt the technology, and availability of funds and financial investment are the top-ranked KSFs contributing to the successful adoption of BIM-DT.

The early detection of SARS-CoV-2, the causative agent of (COVID-19) is now a critical task for the clinical practitioners. The COVID-19 spread is announced as pandemic outbreak between people worldwide by WHO since 11/ March/ 2020. In this consequence, it is top critical priority to become aware of the infected people so that prevention procedures can be processed to minimize the COVID-19 spread and to begin early medical health care of those infected persons. In this paper, the deep studying based totally methodology is usually recommended for the detection of COVID-19 infected patients using X-ray images. The help vector gadget classifies the corona affected X-ray images from others through usage of the deep features. The technique is useful for the clinical practitioners for early detection of COVID-19 infected patients. The suggested system of multi-level thresholding plus SVM presented high accuracy in classification of the infected lung with Covid-19. All images were of the same size and stored in JPEG format with 512 * 512 pixels. The average sensitivity, specificity, and accuracy of the lung classification using the proposed model results were 95.76%, 99.7%, and 97.48%, respectively.

Developing a reliable classification model for drilling pipe stuck is crucial for decision-makers in the petroleum drilling rig. Artificial intelligence (AI) includes several machine learning (ML) algorithms that are used for efficient predictive analytics, optimization, and decision making. Therefore, a comparison analysis for ML models is required to guide practitioners for the appropriate predictive model. Twelve ML techniques are used for drilling pipe stuck such as artificial neural networks, logistic regression, and ensemble methods such as scalable boosting trees and random forest. The drilling cases of the Gulf of Suez wells are collected as an actual dataset for analyzing the ML performance. The key contribution of the study is to automate pipe stuck classification using ML algorithms and mitigate the pipe stuck cases using the genetic algorithm optimization. Out of 12 AI techniques, the results presented that the most reliable algorithm was extremely randomized trees (extra trees) with 100% classification accuracy based on testing dataset. Moreover, this research presents a public open dataset for the drilled wells at the Gulf of Suez to be used for the future experiments, algorithms’ validation, and analysis.