{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T22:55:22Z","timestamp":1776120922680,"version":"3.50.1"},"reference-count":36,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2024,12,12]],"date-time":"2024-12-12T00:00:00Z","timestamp":1733961600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Artif. Intell."],"abstract":"<jats:p>The construction industry is rapidly adopting Industry 4.0 technologies, creating new opportunities to address persistent environmental and operational challenges. This review focuses on how Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) are being leveraged to tackle these issues. It specifically explores AI\u2019s role in predicting air pollution, improving material quality, monitoring worker health and safety, and enhancing Cyber-Physical Systems (CPS) for construction. This study evaluates various AI and ML models, including Artificial Neural Networks (ANNs) and Support Vector Machines SVMs, as well as optimization techniques like whale and moth flame optimization. These tools are assessed for their ability to predict air pollutant levels, improve concrete quality, and monitor worker safety in real time. Research papers were also reviewed to understand AI\u2019s application in predicting the compressive strength of materials like cement mortar, fly ash, and stabilized clay soil. The performance of these models is measured using metrics such as coefficient of determination (<jats:italic>R<\/jats:italic><jats:sup>2<\/jats:sup>), Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). Furthermore, AI has shown promise in predicting and reducing emissions of air pollutants such as PM2.5, PM10, NO<jats:sub>2<\/jats:sub>, CO, SO<jats:sub>2<\/jats:sub>, and O<jats:sub>3<\/jats:sub>. In addition, it improves construction material quality and ensures worker safety by monitoring health indicators like standing postures, electrocardiogram, and galvanic skin response. It is also concluded that AI technologies, including Explainable AI and Petri Nets, are also making advancements in CPS for the construction industry. The models\u2019 performance metrics indicate they are well-suited for real-time construction operations. The study highlights the adaptability and effectiveness of these technologies in meeting current and future construction needs. However, gaps remain in certain areas of research, such as broader AI integration across diverse construction environments and the need for further validation of models in real-world applications. Finally, this research underscores the potential of AI and ML to revolutionize the construction industry by promoting sustainable practices, improving operational efficiency, and addressing safety concerns. It also provides a roadmap for future research, offering valuable insights for industry stakeholders interested in adopting AI technologies.<\/jats:p>","DOI":"10.3389\/frai.2024.1474932","type":"journal-article","created":{"date-parts":[[2024,12,12]],"date-time":"2024-12-12T06:19:19Z","timestamp":1733984359000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":9,"title":["Revolutionizing the construction industry by cutting edge artificial intelligence approaches: a review"],"prefix":"10.3389","volume":"7","author":[{"given":"Eliezer Zahid","family":"Gill","sequence":"first","affiliation":[]},{"given":"Daniela","family":"Cardone","sequence":"additional","affiliation":[]},{"given":"Alessia","family":"Amelio","sequence":"additional","affiliation":[]}],"member":"1965","published-online":{"date-parts":[[2024,12,12]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"194","DOI":"10.1007\/s43452-022-00519-0","article-title":"Hybrid MARS-, MEP-, and ANN-based prediction for modeling the compressive strength of cement mortar with various sand size and clay mineral metakaolin content","volume":"22","author":"Abdalla","year":"2022","journal-title":"Arch. Civil Mech. Eng."},{"key":"ref2","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1007\/s41062-022-00761-8","article-title":"Implementation of multi-expression programming (MEP), artificial neural network (ANN), and M5P-tree to forecast the compression strength of cement-based mortar modified by calcium hydroxide at different mix proportions and curing ages","volume":"7","author":"Abdalla","year":"2022","journal-title":"Innov. Infrastruct. Solut."},{"key":"ref3","doi-asserted-by":"publisher","first-page":"198","DOI":"10.1016\/j.autcon.2016.08.015","article-title":"Smartphone-based construction worker\u2019s activity recognition and classification","volume":"71","author":"Akhavian","year":"2016","journal-title":"Autom. Constr."},{"key":"ref4","first-page":"1","article-title":"Coupling human activity recognition and wearable sensors for data driven construction simulation","volume":"23","author":"Akhavian","year":"2018","journal-title":"J. Inf. Technol. Constr."},{"key":"ref5","doi-asserted-by":"publisher","first-page":"555","DOI":"10.1108\/CI-02-2020-0018","article-title":"Automatic recognition of labor activity: a machine learning approach to capture activity physiological patterns using wearable sensors","volume":"21","author":"Al Jassmi","year":"2020","journal-title":"Constr. Innov."},{"key":"ref6","doi-asserted-by":"publisher","first-page":"661","DOI":"10.1007\/s00500-019-04495-1","article-title":"A new method for prediction of air pollution based on intelligent computation","volume":"24","author":"Al-Janabi","year":"2019","journal-title":"Soft. Comput."},{"key":"ref7","doi-asserted-by":"crossref","DOI":"10.1061\/41109(373)53","article-title":"Towards a cyber-physical systems approach to construction","author":"Anumba","year":"2010"},{"key":"ref8","article-title":"Cyber-physical systems in construction: real-time consistency between virtual models and physical construction","author":"Anumba","year":"2020"},{"key":"ref9","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1016\/j.autcon.2017.03.003","article-title":"Monitoring fatigue in construction workers using physiological measurements","volume":"82","author":"Aryal","year":"2017","journal-title":"Autom. Constr."},{"key":"ref10","doi-asserted-by":"publisher","first-page":"8847","DOI":"10.1007\/s11356-014-2821-z","article-title":"Artificial intelligence modeling to evaluate field performance of photocatalytic asphalt pavement for ambient air purification","volume":"21","author":"Asadi","year":"2014","journal-title":"Environ. Sci. Pollut. Res."},{"key":"ref11","doi-asserted-by":"publisher","first-page":"104440","DOI":"10.1016\/j.autcon.2022.104440","article-title":"Artificial intelligence and smart vision for building and construction 4.0: machine and deep learning methods and applications","volume":"141","author":"Baduge","year":"2022","journal-title":"Autom. Constr."},{"key":"ref12","first-page":"798","article-title":"A Methodology for the Development of Interoperable BIM-based Cyber-Physical Systems, Proceedings of the 35th International Symposium on Automation and Robotics in Construction (ISARC), International Association for Automation and Robotics in Construction (IAARC), July 2018","author":"Correa","year":"2018"},{"key":"ref13","doi-asserted-by":"publisher","first-page":"135504","DOI":"10.1016\/j.jclepro.2022.135504","article-title":"Application of machine learning initiatives and intelligent perspectives for CO2 emissions reduction in construction","volume":"384","author":"Farahzadi","year":"2023","journal-title":"J. Clean. Prod."},{"key":"ref14","doi-asserted-by":"publisher","first-page":"441","DOI":"10.1016\/j.conbuildmat.2017.02.034","article-title":"Implementing ANN to minimize sewage systems concrete corrosion with glass beads substitution","volume":"138","author":"Hendi","year":"2017","journal-title":"Constr. Build. Mater."},{"key":"ref15","doi-asserted-by":"publisher","first-page":"e25997","DOI":"10.1016\/j.heliyon.2024.e25997","article-title":"Hybrid nonlinear regression model versus MARS, MEP, and ANN to evaluate the effect of the size and content of waste tire rubber on the compressive strength of concrete","volume":"10","author":"Jaf","year":"2024","journal-title":"Heliyon"},{"key":"ref16","doi-asserted-by":"publisher","first-page":"111","DOI":"10.22115\/scce.2023.356959.1510","article-title":"Effect of SVM kernel functions on bearing capacity assessment of deep foundations","volume":"7","author":"Jahed Armaghani","year":"2023","journal-title":"J. Soft Comput. Civil Eng."},{"key":"ref17","doi-asserted-by":"publisher","first-page":"1257","DOI":"10.3390\/su9071257","article-title":"Predicting energy consumption and CO2 emissions of excavators in earthwork operations: an artificial neural network model","volume":"9","author":"Jassim","year":"2017","journal-title":"Sustain. For."},{"key":"ref18","doi-asserted-by":"publisher","first-page":"370","DOI":"10.1061\/(ASCE)CP.1943-5487.0000097","article-title":"Accelerometer-based activity recognition in construction","volume":"25","author":"Joshua","year":"2011","journal-title":"J. Comput. Civ. Eng."},{"key":"ref19","doi-asserted-by":"publisher","first-page":"38","DOI":"10.46291\/ICONTECHvol5iss3pp38-47","article-title":"Prediction of labor activity recognition in construction with machine learning algorithms","volume":"5","author":"Karata\u015f","year":"2021","journal-title":"ICONTECH Int. J."},{"key":"ref20","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1016\/j.cemconres.2009.08.022","article-title":"Analysis of carbonation behavior in concrete using neural network algorithm and carbonation modeling","volume":"40","author":"Kwon","year":"2010","journal-title":"Cem. Concr. Res."},{"key":"ref21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2022\/7349001","article-title":"Analyzed and simulated prediction of emission characteristics of construction dust particles under multiple pollution sources","volume":"2022","author":"Liu","year":"2022","journal-title":"Comput. Intell. Neurosci."},{"key":"ref22","doi-asserted-by":"publisher","first-page":"15031","DOI":"10.1007\/s00521-022-07349-4","article-title":"Performance of ANN and M5P-tree to forecast the compressive strength of hand-mix cement-grouted sands modified with polymer using ASTM and BS standards and evaluate the outcomes using SI with OBJ assessments","volume":"34","author":"Mahmood","year":"2022","journal-title":"Neural Comput. Applic."},{"key":"ref23","doi-asserted-by":"publisher","first-page":"220","DOI":"10.3390\/su14010220","article-title":"Introducing VTT-ConIot: a realistic dataset for activity recognition of construction workers using IMU devices","volume":"14","author":"M\u00e4kela","year":"2021","journal-title":"Sustain. For."},{"key":"ref24","doi-asserted-by":"crossref","first-page":"2089","DOI":"10.1109\/MIPRO60963.2024.10569266","article-title":"A deep learning approach for predicting air pollutants on the construction site","volume-title":"2024 47th MIPRO ICT and electronics convention (MIPRO)","author":"Mastromatteo","year":"2024"},{"key":"ref25","doi-asserted-by":"publisher","first-page":"485","DOI":"10.1007\/s12517-021-06712-4","article-title":"Comparison of artificial neural network (ANN) and linear regression modeling with residual errors to predict the unconfined compressive strength and compression index for Erbil City soils, Kurdistan-Iraq","volume":"14","author":"Mawlood","year":"2021","journal-title":"Arab. J. Geosci."},{"key":"ref26","article-title":"ANN model of air quality on the construction site","author":"Milivojevi\u0107","year":"2023"},{"key":"ref27","doi-asserted-by":"publisher","first-page":"12416","DOI":"10.1016\/j.jmrt.2020.08.083","article-title":"ANN, M5P-tree and nonlinear regression approaches with statistical evaluations to predict the compressive strength of cement-based mortar modified with fly ash","volume":"9","author":"Mohammed","year":"2020","journal-title":"J. Mater. Res. Technol."},{"key":"ref28","doi-asserted-by":"publisher","first-page":"3497","DOI":"10.3390\/electronics13173497","article-title":"Explainable AI in manufacturing and industrial cyber\u2013physical systems: a survey","volume":"13","author":"Moosavi","year":"2024","journal-title":"Electronics"},{"key":"ref29","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1007\/s41062-024-01365-0","article-title":"Advanced modeling for predicting compressive strength in fly ash-modified recycled aggregate concrete: XGboost, MEP, MARS, and ANN approaches","volume":"9","author":"Omer","year":"2024","journal-title":"Innov. Infrastruct. Solut."},{"key":"ref30","doi-asserted-by":"publisher","first-page":"7405","DOI":"10.3390\/s23177405","article-title":"Monitoring inattention in construction workers caused by physical fatigue using electrocardiograph (ECG) and galvanic skin response (GSR) sensors","volume":"23","author":"Ouyang","year":"2023","journal-title":"Sensors"},{"key":"ref31","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1061\/41020(339)2","article-title":"Development of human pose analyzing algorithms for the determination of construction productivity in real-time","volume-title":"2009 construction research congress on building a sustainable future","author":"Peddi","year":"2009"},{"key":"ref32","doi-asserted-by":"publisher","first-page":"783","DOI":"10.1007\/s00146-020-01049-0","article-title":"Artificial intelligence in cyber-physical systems","volume":"36","author":"Radanliev","year":"2021","journal-title":"AI & Soc."},{"key":"ref33","doi-asserted-by":"publisher","first-page":"2750","DOI":"10.3390\/su14052750","article-title":"Automated estimation of construction equipment emission using inertial sensors and machine learning models","volume":"14","author":"Shahnavaz","year":"2022","journal-title":"Sustain. For."},{"key":"ref34","doi-asserted-by":"publisher","first-page":"03120002","DOI":"10.1061\/(ASCE)CO.1943-7862.0001843","article-title":"Automated methods for activity recognition of construction workers and equipment: state-of-the-art review","volume":"146","author":"Sherafat","year":"2020","journal-title":"J. Constr. Eng. Manag."},{"key":"ref35","doi-asserted-by":"publisher","first-page":"919","DOI":"10.3390\/buildings12070919","article-title":"A novel combination of PCA and machine learning techniques to select the Most important factors for predicting tunnel construction performance","volume":"12","author":"Wang","year":"2022","journal-title":"Buildings"},{"key":"ref36","doi-asserted-by":"publisher","first-page":"12797","DOI":"10.3390\/su132212797","article-title":"Optimized support vector machines combined with evolutionary random forest for prediction of back-break caused by blasting operation","volume":"13","author":"Yu","year":"2021","journal-title":"Sustain. For."}],"container-title":["Frontiers in Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/frai.2024.1474932\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,12]],"date-time":"2024-12-12T06:19:22Z","timestamp":1733984362000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/frai.2024.1474932\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,12]]},"references-count":36,"alternative-id":["10.3389\/frai.2024.1474932"],"URL":"https:\/\/doi.org\/10.3389\/frai.2024.1474932","relation":{},"ISSN":["2624-8212"],"issn-type":[{"value":"2624-8212","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,12]]},"article-number":"1474932"}}