Machine learning algorithms for safer construction sites: Critical review

Ariticle ID: 544
231 Views, 84 PDF Downloads
Keywords: machine learning (ML); construction; risk management; critical review; Litmaps®; Open knowledge maps®

Abstract

Machine learning, a key thruster of Construction 4.0, has seen exponential publication growth in the last ten years. Many studies have identified ML as the future, but few have critically examined the applications and limitations of various algorithms in construction management. Therefore, this article comprehensively reviewed the top 100 articles from 2018 to 2023 about ML algorithms applied in construction risk management, provided their strengths and limitations, and identified areas for improvement. The study found that integrating various data sources, including historical project data, environmental factors, and stakeholder information, has become a common trend in construction risk. However, the challenges associated with the need for extensive and high-quality datasets, models’ interpretability, and construction projects’ dynamic nature pose significant barriers. The recommendations presented in this paper can facilitate interdisciplinary collaboration between traditional construction and machine learning, thereby enhancing the development of specialized algorithms for real-world projects.

References

[1] Liu R, Liu HC, Shi H, et al. Occupational health and safety risk assessment: A systematic literature review of models, methods, and applications. Safety Science. 2023; 160: 106050. doi: 10.1016/j.ssci.2022.106050

[2] Pinto A, Nunes IL, Ribeiro RA. Occupational risk assessment in construction industry – Overview and reflection. Safety Science. 2011; 49(5): 616-624. doi: 10.1016/j.ssci.2011.01.003

[3] ILOSTAT. ILOSTAT data tools to find and download labor statistics. Available online: https://ilostat.ilo.org/data (accessed on 22 December 2023).

[4] Hastak M, Shaked A. ICRAM-1: Model for International Construction Risk Assessment. Journal of Management in Engineering. 2020; 16(1): 59–69.

[5] Lee HS, Kim H, Park M, Ai Lin Teo E, Lee KP. Construction Risk Assessment Using Site Influence Factors. Journal of Computing in Civil Engineering. 2012; 26(3): 319–330.

[6] Mulholland B, Christian J. Risk Assessment in Construction Schedules. Journal of Construction Engineering and Management. 1999; 125(1): 8–15.

[7] Ashtari MA, Ansari R, Hassannayebi E, et al. Cost Overrun Risk Assessment and Prediction in Construction Projects: A Bayesian Network Classifier Approach. Buildings. 2022; 12(10): 1660. doi: 10.3390/buildings12101660

[8] Mudiyanselage SE, Nguyen PHD, Rajabi MS, et al. Automated Workers’ Ergonomic Risk Assessment in Manual Material Handling Using sEMG Wearable Sensors and Machine Learning. Electronics. 2021; 10(20): 2558. doi: 10.3390/electronics10202558

[9] Gondia A, Siam A, El-Dakhakhni W, Nassar AH. Machine Learning Algorithms for Construction Projects Delay Risk Prediction. Journal of Construction Engineering and Management. 2020; 146(1): 04019085.

[10] Huang J, Zeng X, Fu J, et al. Safety Risk Assessment Using a BP Neural Network of High Cutting Slope Construction in High-Speed Railway. Buildings. 2022; 12(5): 598. doi: 10.3390/buildings12050598

[11] Ni G, Fang Y, Niu M, et al. Spatial differences, dynamic evolution and influencing factors of China's construction industry carbon emission efficiency. Journal of Cleaner Production. 2024; 448: 141593. doi: 10.1016/j.jclepro.2024.141593

[12] Verma A, Prakash S, Kumar A. AI-based Building Management and Information System with Multi-agent Topology for an Energy-efficient Building: Towards Occupants Comfort. IETE Journal of Research. 2020; 69(2): 1033-1044. doi: 10.1080/03772063.2020.1847701

[13] Kangari R, Riggs LS. Construction risk assessment by linguistics. IEEE Transactions on Engineering Management. 1989; 36(2): 126-131. doi: 10.1109/17.18829

[14] Lin SS, Shen SL, Zhou A, et al. Risk assessment and management of excavation system based on fuzzy set theory and machine learning methods. Automation in Construction. 2021; 122: 103490. doi: 10.1016/j.autcon.2020.103490

[15] Taroun A. Towards a better modelling and assessment of construction risk: Insights from a literature review. International Journal of Project Management. 2014; 32(1): 101-115. doi: 10.1016/j.ijproman.2013.03.004

[16] KarimiAzari A, Mousavi N, Mousavi SF, et al. Risk assessment model selection in construction industry. Expert Systems with Applications. 2011; 38(8): 9105-9111. doi: 10.1016/j.eswa.2010.12.110

[17] Subramanyan H, Sawant PH, Bhatt V. Construction Project Risk Assessment: Development of Model Based on Investigation of Opinion of Construction Project Experts from India. Journal of Construction Engineering and Management. 2012; 138(3): 409–421.

[18] Hartmann T, Trappey A. Advanced Engineering Informatics - Philosophical and methodological foundations with examples from civil and construction engineering. Developments in the Built Environment. 2020; 4: 100020. doi: 10.1016/j.dibe.2020.100020

[19] Sun H, Burton HV, Huang H. Machine learning applications for building structural design and performance assessment: State-of-the-art review. Journal of Building Engineering. 2021; 33: 101816. doi: 10.1016/j.jobe.2020.101816

[20] Wang X, Mazumder RK, Salarieh B, et al. Machine Learning for Risk and Resilience Assessment in Structural Engineering: Progress and Future Trends. Journal of Structural Engineering. 2022; 148(8).

[21] Zhang J, Zi L, Hou Y, et al. A C-BiLSTM Approach to Classify Construction Accident Reports. Applied Sciences. 2020; 10(17): 5754. doi: 10.3390/app10175754

[22] Butler KT, Davies DW, Cartwright H, et al. Machine learning for molecular and materials science. Nature. 2018; 559(7715): 547-555. doi: 10.1038/s41586-018-0337-2

[23] Zhong M, Tran K, Min Y, et al. Accelerated discovery of CO2 electrocatalysts using active machine learning. Nature. 2020; 581(7807): 178-183. doi: 10.1038/s41586-020-2242-8

[24] Warnat-Herresthal S, Schultze H, Shastry KL, et al. Swarm Learning for decentralized and confidential clinical machine learning. Nature. 2021; 594(7862): 265-270. doi: 10.1038/s41586-021-03583-3

[25] Sun D, Wen H, Wang D, et al. A random forest model of landslide susceptibility mapping based on hyperparameter optimization using Bayes algorithm. Geomorphology. 2020; 362: 107201. doi: 10.1016/j.geomorph.2020.107201

[26] Akinosho TD, Oyedele LO, Bilal M, et al. Deep learning in the construction industry: A review of present status and future innovations. Journal of Building Engineering. 2020; 32: 101827. doi: 10.1016/j.jobe.2020.101827

[27] Alpaydın E. Machine Learning. Mit Press; 2021.

[28] Hegde J, Rokseth B. Applications of machine learning methods for engineering risk assessment – A review. Safety Science. 2020; 122: 104492. doi: 10.1016/j.ssci.2019.09.015

[29] Maps OK. Open Knowledge Maps—A visual interface to the world’s scientific knowledge. Open Knowledge Maps; 2023.

[30] Litmaps. app.litmaps.com. Available online: https://app.litmaps.com/seed (accessed on 22 December 2023).

[31] Tessema AT, Alene GA, Wolelaw NM. Assessment of risk factors on construction projects in gondar city, Ethiopia. Heliyon. 2022; 8(11): e11726. doi: 10.1016/j.heliyon.2022.e11726

[32] Zhu Z, Sun J, Li X. An construction method of scorecard using machine learning and logical regression. Procedia Computer Science. 2022; 214: 1541-1545. doi: 10.1016/j.procs.2022.11.341

[33] Gariazzo C, Taiano L, Bonafede M, et al. Association between extreme temperature exposure and occupational injuries among construction workers in Italy: An analysis of risk factors. Environment International. 2023; 171: 107677. doi: 10.1016/j.envint.2022.107677

[34] Hemasinghe H, Rangali RSS, Deshapriya NL, et al. Landslide susceptibility mapping using logistic regression model (a case study in Badulla District, Sri Lanka). Procedia Engineering. 2018; 212: 1046-1053. doi: 10.1016/j.proeng.2018.01.135

[35] Li N, Jimenez R. A logistic regression classifier for long-term probabilistic prediction of rock burst hazard. Natural Hazards. 2017; 90(1): 197-215. doi: 10.1007/s11069-017-3044-7

[36] Xie P, Zhang R, Zheng J, et al. Probabilistic analysis of subway station excavation based on BIM-RF integrated technology. Automation in Construction. 2022; 135: 104114. doi: 10.1016/j.autcon.2021.104114

[37] Hu W, Zhang S, Fu Y, et al. Objective diagnosis of machine learning method applicability to land comprehensive carrying capacity evaluation: A case study based on integrated RF and DPSIR models. Ecological Indicators. 2023; 151: 110338. doi: 10.1016/j.ecolind.2023.110338

[38] Wang R, Asghari V, Hsu SC, et al. Detecting corporate misconduct through random forest in China’s construction industry. Journal of Cleaner Production. 2020; 268: 122266. doi: 10.1016/j.jclepro.2020.122266

[39] Wu X, Wang L, Chen B, et al. Multi-objective optimization of shield construction parameters based on random forests and NSGA-II. Advanced Engineering Informatics. 2022; 54: 101751. doi: 10.1016/j.aei.2022.101751

[40] Wen H, Wu J, Zhang C, et al. Hybrid optimized RF model of seismic resilience of buildings in mountainous region based on hyperparameter tuning and SMOTE. Journal of Building Engineering. 2023; 71: 106488. doi: 10.1016/j.jobe.2023.106488

[41] Hu R, Chen K, Chen W, et al. Estimation of construction waste generation based on an improved on-site measurement and SVM-based prediction model: A case of commercial buildings in China. Waste Management. 2021; 126: 791-799. doi: 10.1016/j.wasman.2021.04.012

[42] Chen JH, Lin JZ. Developing an SVM based risk hedging prediction model for construction material suppliers. Automation in Construction. 2010; 19(6): 702-708. doi: 10.1016/j.autcon.2010.02.014

[43] Tserng H P, Lin G F, Tsai L K, et al. An enforced support vector machine model for construction contractor default prediction. Automation in Construction, 2011; 20(8): 1242-1249. doi: 10.1016/j.autcon.2011.05.007

[44] Fan M, Sharma A. Design and implementation of construction cost prediction model based on SVM and LSSVM in industries 4.0. International Journal of Intelligent Computing and Cybernetics. 2021; 14(2): 145-157. doi: 10.1108/ijicc-10-2020-0142

[45] Fu W, Zhang H, Huang F. Internet-based supply chain financing-oriented risk assessment using BP neural network and SVM. PLOS ONE. 2022; 17(1): e0262222. doi: 10.1371/journal.pone.0262222

[46] Mostofi F, Toğan V, Ayözen YE, et al. Construction Safety Risk Model with Construction Accident Network: A Graph Convolutional Network Approach. Sustainability. 2022; 14(23): 15906. doi: 10.3390/su142315906

[47] Khalili M A, Guerriero L, Pouralizadeh M, et al. Monitoring and prediction of landslide-related deformation based on the GCN-LSTM algorithm and SAR imagery. Natural Hazards; 2023; 119(1): 39-68. doi: 10.1007/s11069-023-06121-8

[48] Mostofi F, Toğan V. Predicting Construction Accident Outcomes Using Graph Convolutional and Dual-Edge Safety Networks. Arabian Journal for Science and Engineering. Published online December 28, 2023. doi: 10.1007/s13369-023-08609-8

[49] Fu X, Pan Y, Zhang L. A causal-temporal graphic convolutional network (CT-GCN) approach for TBM load prediction in tunnel excavation. Expert Systems with Applications. 2024; 238: 121977. doi: 10.1016/j.eswa.2023.121977

[50] Li P, Wu F, Xue S, et al. Study on the Interaction Behaviors Identification of Construction Workers Based on ST-GCN and YOLO. Sensors. 2023; 23(14): 6318. doi: 10.3390/s23146318

[51] Zhang Y, Du Z, Hu L. A construction method of urban road risky vehicles based on dynamic knowledge graph. Electronic Research Archive. 2023; 31(7): 3776-3790. doi: 10.3934/era.2023192

[52] Chen JH, Hsu SC, Luo YH, Skibniewski MJ. Knowledge Management for Risk Hedging by Construction Material Suppliers.” Journal of Management in Engineering. 2012; 28(3): 273–280.

[53] Pandey P, Bandhu KC. A credit risk assessment on borrowers classification using optimized decision tree and KNN with bayesian optimization. International Journal of Information Technology. 2022; 14(7): 3679-3689. doi: 10.1007/s41870-022-00974-1

[54] Jaber F K, Al-Zwainy F M S, Hachem S W. Optimizing of predictive performance for construction projects utilizing support vector machine technique. Cogent Engineering, 2019; 6(1): 1685860. doi: 10.1080/23311916.2019.1685860

[55] Chenzhong R, Wenliang K, Taihua Z, et al. Intelligent Generation and Analysis of the Municipal Road Construction Scheme Based on the KNN Algorithm. Mathematical Problems in Engineering, 2022. doi: 10.1155/2022/8752870

[56] Li L, Wu Y, Huang Y, et al. Optimized Apriori algorithm for deformation response analysis of landslide hazards. Computers & Geosciences. 2023; 170: 105261. doi: 10.1016/j.cageo.2022.105261

[57] Chen B, Wei N, Qu T, et al. Research on weighting method of geological hazard susceptibility evaluation index based on Apriori Algorithm. Frontiers in Earth Science. 2023; 11. doi: 10.3389/feart.2023.1127889

[58] Chen Q, Tian Z, Lei T, et al. An association rule mining model for evaluating the potential correlation of construction cross operation risk. Engineering, Construction and Architectural Management. 2022; 30(10): 5109-5132. doi: 10.1108/ecam-09-2021-0792

[59] Shao B, Hu Z, Liu D. Using Improved Principal Component Analysis to Explore Construction Accident Situations from the Multi-Dimensional Perspective: A Chinese Study. International Journal of Environmental Research and Public Health. 2019; 16(18): 3476–3476.

[60] Xiang P, Jia F, Li X. Critical Behavioral Risk Factors among Principal Participants in the Chinese Construction Industry. Sustainability. 2018; 10(9): 3158. doi: 10.3390/su10093158

[61] Siddiqui F, Sargent P, Montague G. The use of PCA and signal processing techniques for processing time-based construction settlement data of road embankments. Advanced Engineering Informatics. 2020; 46: 101181. doi: 10.1016/j.aei.2020.101181

[62] Yan H, He Z, Gao C, et al. Investment estimation of prefabricated concrete buildings based on XGBoost machine learning algorithm. Advanced Engineering Informatics. 2022; 54: 101789. doi: 10.1016/j.aei.2022.101789

[63] Cherif IL, Kortebi A. On using Extreme Gradient Boosting (XGBoost) Machine Learning algorithm for Home Network Traffic Classification. IEEE Xplore; 2019.

[64] Coffie GH, Cudjoe SKF. Using extreme gradient boosting (XGBoost) machine learning to predict construction cost overruns. International Journal of Construction Management. 2023.

[65] Liang W, Luo S, Zhao G, et al. Predicting hard rock pillar stability using GBDT, XGBoost, and LightGBM algorithms. Mathematics, 2020; 8(5): 765. doi: 10.3390/math8050765

[66] Liu P, Li Y. An improved failure mode and effect analysis method for multi-criteria group decision-making in green logistics risk assessment. Reliability Engineering & System Safety. 2021; 215: 107826. doi: 10.1016/j.ress.2021.107826

[67] Wang G, Liu M, Cao D, et al. Identifying high-frequency–low-severity construction safety risks: an empirical study based on official supervision reports in Shanghai. Engineering, Construction and Architectural Management. 2021; 29(2): 940-960. doi: 10.1108/ecam-07-2020-0581

[68] Ayhan BU, Tokdemir OB. Accident Analysis for Construction Safety Using Latent Class Clustering and Artificial Neural Networks. Journal of Construction Engineering and Management. 2020; 146(3): 04019114.

[69] Kim S, Choi CY, Shahandashti M, Rok Ryu K. Improving Accuracy in Predicting City-Level Construction Cost Indices by Combining Linear ARIMA and Nonlinear ANNs. Journal of Management in Engineering. 2022; 38(2).

[70] Moon S, Chi S, Kim DY. Predicting Construction Cost Index Using the Autoregressive Fractionally Integrated Moving Average Model. Journal of Management in Engineering. 2018; 34(2): 04017063.

[71] Ghashghaie M, Nozari H. Effect of Dam Construction on Lake Urmia: Time Series Analysis of Water Level via ARIMA. Journal of Agricultural Science and Technology. 2018; 20(7): 1541–1553.

[72] Kaloop MR, Eldiasty M, Hu JW. Safety and reliability evaluations of bridge behaviors under ambient truck loads through structural health monitoring and identification model approaches. Measurement. 2022; 187: 110234. doi: 10.1016/j.measurement.2021.110234

[73] Hajifar S, Sun H, Megahed FM, et al. A forecasting framework for predicting perceived fatigue: Using time series methods to forecast ratings of perceived exertion with features from wearable sensors. Applied Ergonomics. 2021; 90: 103262. doi: 10.1016/j.apergo.2020.103262

[74] Lam TYM, Siwingwa N. Risk management and contingency sum of construction projects. Journal of Financial Management of Property and Construction. 2017; 22(3): 237-251. doi: 10.1108/jfmpc-10-2016-0047

[75] Montgomery D C, Peck E A, Vining G G. Introduction to linear regression analysis. John Wiley & Sons, 2021.

[76] Huang CH, Hsieh SH. Predicting BIM labor cost with random forest and simple linear regression. Automation in Construction. 2020; 118: 103280. doi: 10.1016/j.autcon.2020.103280

[77] Esmaeili B, Hallowell MR, Rajagopalan B. Attribute-Based Safety Risk Assessment. II: Predicting Safety Outcomes Using Generalized Linear Models. Journal of Construction Engineering and Management. 2015; 141(8): 04015022.

[78] Lyu J. Construction of Enterprise Financial Early Warning Model Based on Logistic Regression and BP Neural Network. Computational Intelligence and Neuroscience. 2022; 2022: 1-7. doi: 10.1155/2022/2614226

[79] Levy A, Baha R. Credit risk assessment: a comparison of the performances of the linear discriminant analysis and the logistic regression. International Journal of Entrepreneurship and Small Business. 2021; 42(1/2): 169. doi: 10.1504/ijesb.2021.112265

[80] Akboğa Kale Ö, Baradan S. Identifying Factors that Contribute to Severity of Construction Injuries using Logistic Regression Model. Teknik Dergi. 2020; 31(2): 9919-9940. doi: 10.18400/tekderg.470633

[81] Bhattacharjee P, Dey V, Mandal UK. Risk assessment by failure mode and effects analysis (FMEA) using an interval number based logistic regression model. Safety Science. 2020; 132: 104967. doi: 10.1016/j.ssci.2020.104967

[82] Wong CH. Contractor Performance Prediction Model for the United Kingdom Construction Contractor: Study of Logistic Regression Approach. Journal of Construction Engineering and Management. 2004; 130(5): 691–698.

[83] Zhang X, Huang S, Yang S, et al. Safety Assessment in Road Construction Work System Based on Group AHP-PCA. Mathematical Problems in Engineering. 2020; 2020: 1-12. doi: 10.1155/2020/6210569

[84] Bai L, Song C, Zhou X, et al. Assessing project portfolio risk via an enhanced GA-BPNN combined with PCA. Engineering Applications of Artificial Intelligence. 2023; 126: 106779. doi: 10.1016/j.engappai.2023.106779

[85] Shi H, Li W, Deng Y. Applying Principal Component Analysis and Unascertained Method for the Analysis of Construction Accident Risk. J. Comput., 2010; 5(8): 1273-1280. doi: 10.4304/jcp.5.8.1273-1280

[86] Wang G, Ma J. A hybrid ensemble approach for enterprise credit risk assessment based on Support Vector Machine. Expert Systems with Applications, 2012; 39(5): 5325-5331. 10.1016/j.eswa.2011.11.003

[87] Khemakhem S, Ben Said F, Boujelbene Y. Credit risk assessment for unbalanced datasets based on data mining, artificial neural network and support vector machines. Journal of Modelling in Management. 2018; 13(4): 932-951. doi: 10.1108/jm2-01-2017-0002

[88] Mangeli M, Shahraki A, Saljooghi FH. Improvement of risk assessment in the FMEA using nonlinear model, revised fuzzy TOPSIS, and support vector machine. International Journal of Industrial Ergonomics. 2019; 69: 209-216. doi: 10.1016/j.ergon.2018.11.004

[89] Steinwart I. Support vector machines. Springer; 2014.

[90] Noble W S. What is a support vector machine?. Nature biotechnology, 2006; 24(12): 1565-1567. doi: 10.1038/nbt1206-1565

[91] Yang R, Feng J, Sun Y. Construction and Classification Prediction of Risk Assessment Iindicators for Water Environment Treatment PPP Projects. 2023. doi: 10.21203/rs.3.rs-2845690/v1

[92] Zhang L, Hu H, Zhang D. A credit risk assessment model based on SVM for small and medium enterprises in supply chain finance. Financial Innovation. 2015; 1(1). doi: 10.1186/s40854-015-0014-5

[93] Gong P, Guo H, Huang Y, et al. Safety risk evaluations of deep foundation construction schemes based on imbalanced data sets. Journal of civil engineering and management. 2020; 26(4): 380–395.

[94] Liu P, Xie M, Bian J, et al. A Hybrid PSO–SVM Model Based on Safety Risk Prediction for the Design Process in Metro Station Construction. International Journal of Environmental Research and Public Health. 2020; 17(5): 1714. doi: 10.3390/ijerph17051714

[95] Wei Y, Zhang J, Wang J. Research on Building Fire Risk Fast Assessment Method Based on Fuzzy comprehensive evaluation and SVM. Procedia Engineering. 2018; 211: 1141-1150. doi: 10.1016/j.proeng.2017.12.121

[96] Chang YC, Chang KH, Wu GJ. Application of Extreme gradient boosting trees in the construction of credit risk assessment models for financial institutions. Applied Soft Computing. 2018; 73: 914-920. doi: 10.1016/j.asoc.2018.09.029

[97] Li Z. GBDT-SVM Credit Risk Assessment Model and Empirical Analysis of Peer-to-Peer Borrowers under Consideration of Audit Information. Open Journal of Business and Management. 2018; 6(2): 362-372. doi: 10.4236/ojbm.2018.62026

[98] Zhang H, Shi Y, Yang X, et al. A firefly algorithm modified support vector machine for the credit risk assessment of supply chain finance. Research in International Business and Finance. 2021; 58: 101482. doi: 10.1016/j.ribaf.2021.101482

[99] Yin Q, Zhou J, Zhou Y, et al. Construction safety risk assessment method of construction engineering based on improved SVM. International Journal of Sustainable Development, 2023; 26(3-4): 329-343. doi: 10.1504/IJSD.2023.10058663155

[100] Chen J, Liu L, Pei J, et al. An ensemble risk assessment model for urban rainstorm disasters based on random forest and deep belief nets: a case study of Nanjing, China. Natural Hazards. 2021; 107(3): 2671-2692. doi: 10.1007/s11069-021-04630-y

[101] Tang L, Cai F, Ouyang Y. Applying a nonparametric random forest algorithm to assess the credit risk of the energy industry in China. Technological Forecasting and Social Change. 2019; 144: 563-572. doi: 10.1016/j.techfore.2018.03.007

[102] Liu W, Zhang Y, Liang Y, et al. Landslide Risk Assessment Using a Combined Approach Based on InSAR and Random Forest. Remote Sensing. 2022; 14(9): 2131. doi: 10.3390/rs14092131

[103] Zermane A, Mohd Tohir MZ, Zermane H, et al. Predicting fatal fall from heights accidents using random forest classification machine learning model. Safety Science. 2023; 159: 106023. doi: 10.1016/j.ssci.2022.106023

[104] Kang K, Ryu H. Predicting types of occupational accidents at construction sites in Korea using random forest model. Safety Science. 2019; 120: 226-236. doi: 10.1016/j.ssci.2019.06.034

[105] Zhou Y, Li S, Zhou C, Luo H. Intelligent Approach Based on Random Forest for Safety Risk Prediction of Deep Foundation Pit in Subway Stations. Journal of Computing in Civil Engineering. 2019; 33(1).

[106] Zhang H, Shi Y, Tong J. Online supply chain financial risk assessment based on improved random forest. Journal of Data, Information and Management. 2021; 3(1): 41-48. doi: 10.1007/s42488-021-00042-6

[107] Zhu Z, Zhang Y. Flood disaster risk assessment based on random forest algorithm. Neural Computing and Applications. 2021; 34(5): 3443-3455. doi: 10.1007/s00521-021-05757-6

[108] Wang Y, Wen H, Sun D, et al. Quantitative Assessment of Landslide Risk Based on Susceptibility Mapping Using Random Forest and GeoDetector. Remote Sensing. 2021; 13(13): 2625. doi: 10.3390/rs13132625

[109] Aprillia H, Yang HT, Huang CM. Statistical Load Forecasting Using Optimal Quantile Regression Random Forest and Risk Assessment Index. IEEE Transactions on Smart Grid. 2021; 12(2): 1467-1480. doi: 10.1109/tsg.2020.3034194

[110] Armaghani DJ, Mahdiyar A, Hasanipanah M, et al. Risk Assessment and Prediction of Flyrock Distance by Combined Multiple Regression Analysis and Monte Carlo Simulation of Quarry Blasting. Rock Mechanics and Rock Engineering. 2016; 49(9): 3631-3641. doi: 10.1007/s00603-016-1015-z

[111] Liu Y, Chen H, Zhang L, et al. Risk prediction and diagnosis of water seepage in operational shield tunnels based on random forest. Journal of civil engineering and management. 2021; 27(7): 539–552.

[112] Ghosh S, Das A. Wetland conversion risk assessment of East Kolkata Wetland: A Ramsar site using random forest and support vector machine model. Journal of Cleaner Production. 2020; 275: 123475. doi: 10.1016/j.jclepro.2020.123475

[113] Langroodi AK, Vahdatikhaki F, Doree A. Activity recognition of construction equipment using fractional random forest. Automation in Construction. 2021; 122: 103465. doi: 10.1016/j.autcon.2020.103465

[114] Junjia Y, Alias A H, Haron N A, et al. A Bibliometrics-Based Systematic Review of Safety Risk Assessment for IBS Hoisting Construction. Buildings. 2023; 13(7): 1853. doi: 10.3390/buildings13071853

[115] Han J, Kim J, Park S, et al. Seismic Vulnerability Assessment and Mapping of Gyeongju, South Korea Using Frequency Ratio, Decision Tree, and Random Forest. Sustainability. 2020; 12(18): 7787. doi: 10.3390/su12187787

[116] Lee YYR, Samad H, Miang Goh Y. Perceived Importance of Authentic Learning Factors in Designing Construction Safety Simulation Game-Based Assignment: Random Forest Approach. Journal of Construction Engineering and Management. 2020; 146(3): 04020002.

[117] Shoar S, Chileshe N, Edwards JD. Machine learning-aided engineering services’ cost overruns prediction in high-rise residential building projects: Application of random forest regression. Journal of Building Engineering. 2022; 50: 104102. doi: 10.1016/j.jobe.2022.104102

[118] Chen J, Li Q, Wang H, et al. A Machine Learning Ensemble Approach Based on Random Forest and Radial Basis Function Neural Network for Risk Evaluation of Regional Flood Disaster: A Case Study of the Yangtze River Delta, China. International Journal of Environmental Research and Public Health. 2019; 17(1): 49. doi: 10.3390/ijerph17010049

[119] Karabadji NEI, Amara Korba A, Assi A, et al. Accuracy and diversity-aware multi-objective approach for random forest construction. Expert Systems with Applications. 2023; 225: 120138. doi: 10.1016/j.eswa.2023.120138

[120] Fouad Sabry. K Nearest Neighbor Algorithm. One Billion Knowledgeable; 2023.

[121] Lee KP, Lee HS, Park M, Kim DY, Jung M. Management-Reserve Estimation for International Construction Projects Based on Risk-Informed k-NN. Journal of Management in Engineering. 2017; 33(4): 04017002.

[122] Kamran M, Ullah B, Ahmad M, et al. Application of KNN-based Isometric Mapping and Fuzzy C-Means Algorithm to Predict Short-term Rockburst Risk in Deep Underground Projects. Published online October 6, 2022. doi: 10.21203/rs.3.rs-2128698/v1

[123] Liu X, Xu F, Zhang Z, et al. Fall-portent detection for construction sites based on computer vision and machine learning. Engineering, Construction and Architectural Management. Published online October 12, 2023. doi: 10.1108/ecam-05-2023-0458

[124] Sanni-Anibire M O, Zin R M, Olatunji S O. Machine learning model for delay risk assessment in tall building projects. International Journal of Construction Management, 2022; 22(11): 2134-2143. doi: 10.1080/15623599.2020.1768326

[125] Burns JJR, Shealy BT, Greer MS, et al. Addressing noise in co-expression network construction. Briefings in Bioinformatics. 2021; 23(1). doi: 10.1093/bib/bbab495

[126] Zhong G, Lu G, Liu M, Cui M. A novel risk assessment system for port state control inspection. In: Proceedings of the 2008 IEEE International Conference on Intelligence and Security Informatics.

[127] Arabiat A, Al-Bdour H, Bisharah M. Predicting the construction projects time and cost overruns using K-nearest neighbor and artificial neural network: a case study from Jordan. Asian Journal of Civil Engineering. 2023; 24(7): 2405-2414. doi: 10.1007/s42107-023-00649-7

[128] Zhang Y, Ding L, Peter ED. Planning of Deep Foundation Construction Technical Specifications Using Improved Case-Based Reasoning with Weighted k-Nearest Neighbors. American Society of Civil Engineers. 2017; 31(5).

[129] Brownlee J. XGBoost With Python. Machine Learning Mastery; 2016.

[130] Qin R. The Construction of Corporate Financial Management Risk Model Based on XGBoost Algorithm. Chen M, ed. Journal of Mathematics. 2022; 2022: 1-8. doi: 10.1155/2022/2043369

[131] Liu W, Chen Z, Hu Y. XGBoost algorithm-based prediction of safety assessment for pipelines. International Journal of Pressure Vessels and Piping. 2022; 197: 104655. doi: 10.1016/j.ijpvp.2022.104655

[132] Wang Y, Ni X S. A XGBoost risk model via feature selection and Bayesian hyper-parameter optimization. arXiv preprint arXiv:1901.08433, 2019. doi: 10.48550/arXiv.1901.08433

[133] Shi L, Qian C, Guo F. Real-time driving risk assessment using deep learning with XGBoost. Accident Analysis & Prevention. 2022; 178: 106836. doi: 10.1016/j.aap.2022.106836

[134] Luo H, Yang Q, Wang W, et al. XGBoost‑based assessment method for fire risk levels of transmission lines. Journal of Electric Power Science and Technology, 2024; 38(6): 132-141. doi: 10.19781/j.issn.1673-9140.2023.06.014

[135] Shehadeh A, Alshboul O, Al Mamlook RE, et al. Machine learning models for predicting the residual value of heavy construction equipment: An evaluation of modified decision tree, LightGBM, and XGBoost regression. Automation in Construction. 2021; 129: 103827. doi: 10.1016/j.autcon.2021.103827

[136] Hamerly G, Elkan C. Learning the k in k-means. Advances in neural information processing systems, 2003; 16.

[137] Er Kara M, Oktay Fırat S. Supplier Risk Assessment Based on Best-Worst Method and K-Means Clustering: A Case Study. Sustainability. 2018; 10(4): 1066. doi: 10.3390/su10041066

[138] Chattapadhyay DB, Putta J, Rao RMP. Risk Identification, Assessments, and Prediction for Mega Construction Projects: A Risk Prediction Paradigm Based on Cross Analytical-Machine Learning Model. Buildings. 2021; 11(4): 172.

[139] Tao Y, Yong X, Yang J, et al. Risk Early-Warning Framework for Government-Invested Construction Project Based on Fuzzy Theory, Improved BPNN, and K-Means. Mathematical Problems in Engineering. 2022; 2022: 1-19. doi: 10.1155/2022/5958472

[140] Shumway R H, Stoffer D S, Shumway R H, et al. ARIMA models. Time series analysis and its applications: with R examples, 2017; 75-163. doi: 10.1007/978-3-319-52452-8_3

[141] Cao H, Goh YM. Analyzing construction safety through time series methods. Frontiers of Engineering Management. 2019; 6(2): 262-274. doi: 10.1007/s42524-019-0015-6

[142] Li M, Baek M, Ashuri B. Forecasting Ratio of Low Bid to Owner’s Estimate for Highway Construction. Journal of Construction Engineering and Management. 2021; 147(1).

[143] Yi F, Zeng H, Liu T, Wu Y. Research on Cement Price Fluctuation Prediction Based on EEMD-ARIMA. Lecture Notes in Operations Research. 2023.

[144] Liu Z, Zhou J. Introduction to Graph Neural Networks. Springer International Publishing; 2020. doi: 10.1007/978-3-031-01587-8

[145] Fu X, Wu M, Ponnarasu S, et al. A hybrid deep learning approach for dynamic attitude and position prediction in tunnel construction considering spatio-temporal patterns. Expert Systems with Applications. 2023; 212: 118721. doi: 10.1016/j.eswa.2022.118721

[146] Pan X, Zhong B, Wang Y, et al. Identification of accident-injury type and bodypart factors from construction accident reports: A graph-based deep learning framework. Advanced Engineering Informatics. 2022; 54: 101752. doi: 10.1016/j.aei.2022.101752

[147] Junjia Y, Alias A H, Haron N A, et al. Identification and analysis of hoisting safety risk factors for IBS construction based on the AcciMap and cases study. Heliyon, 2024; 10(1). doi: 10.1016/j.heliyon.2023.e23587

[148] Mostofi F, Toğan V. Construction safety predictions with multi-head attention graph and sparse accident networks. Automation in Construction. 2023; 156: 105102. doi: 10.1016/j.autcon.2023.105102

[149] Xue G, Liu S, Ren L, et al. Risk assessment of utility tunnels through risk interaction-based deep learning. Reliability Engineering & System Safety. 2024; 241: 109626. doi: 10.1016/j.ress.2023.109626

[150] Zhu W, Shi D, Cheng R, et al. Human risky behaviour recognition during ladder climbing based on multi-modal feature fusion and adaptive graph convolutional network. Signal, Image and Video Processing, 2024: 1-11. doi: 10.1007/s11760-023-02923-2

[151] Xie X, Fu G, Xue Y, et al. Risk prediction and factors risk analysis based on IFOA-GRNN and apriori algorithms: Application of artificial intelligence in accident prevention. Process Safety and Environmental Protection. 2019; 122: 169-184. doi: 10.1016/j.psep.2018.11.019

[152] Deng Y, Zhang Y, Yuan Z, et al. Analyzing Subway Operation Accidents Causations: Apriori Algorithm and Network Approaches. International Journal of Environmental Research and Public Health. 2023; 20(4): 3386. doi: 10.3390/ijerph20043386

[153] Junjia Y, Alias A H, Haron N A, et al. A Bibliometric Review on Safety Risk Assessment of Construction Based on CiteSpace Software and WoS Database. Sustainability, 2023; 15(15): 11803. doi: 10.3390/su151511803

[154] Sarkar S, Ejaz N, Maiti J, et al. An integrated approach using growing self-organizing map-based genetic K-means clustering and tolerance rough set in occupational risk analysis. Neural Computing and Applications. 2022; 34(12): 9661-9687. doi: 10.1007/s00521-022-06956-5

[155] Junjia Y, Alias A H, Haron N A, et al. Trend Analysis of Marine Construction Disaster Prevention Based on Text Mining: Evidence from China. Sustainable Marine Structures, 2024; 6(1): 20-32. doi: 10.36956/sms.v6i1.1026

[156] Verma A, Prakash S, Srivastava V, et al. Sensing, Controlling, and IoT Infrastructure in Smart Building: A Review. IEEE Sensors Journal. 2019; 19(20): 9036-9046. doi: 10.1109/jsen.2019.2922409

[157] Feng S, He X, Xu H, Armaghani DJ, Sheng D. Applications of Machine Learning in Mechanised Tunnel Construction: A Systematic Review. Eng. 2023; 4(2): 1516–1535.

[158] Zhu Z, Jeelani I, Gheisari M. Physical risk assessment of drone integration in construction using 4D simulation. Automation in Construction. 2023; 156: 105099. doi: 10.1016/j.autcon.2023.105099

[159] Zhong J, Mao Y, Yuan X. Lifetime seismic risk assessment of bridges with construction and aging considerations. Structures. 2023; 47: 2259-2272. doi: 10.1016/j.istruc.2022.12.035

[160] Nguyen HD, Do QNH, Macchion L. Influence of practitioners’ characteristics on risk assessment in Green Building projects in emerging economies: a case of Vietnam. Engineering, Construction and Architectural Management. 2021; 30(2): 833-852. doi: 10.1108/ecam-05-2021-0436

[161] Sohrabi H, Noorzai E. Risk assessment in Iranian oil and gas construction industry: a process approach. International Journal of Quality & Reliability Management. 2021; 40(1): 124-147. doi: 10.1108/ijqrm-03-2021-0069

[162] Hatamleh MT, Moynihan GP, Batson RG, et al. Risk assessment and ranking in the developing countries’ construction industry: the case of Jordan. Engineering, Construction and Architectural Management. 2021; 30(4): 1344-1364. doi: 10.1108/ecam-06-2021-0489

[163] Al-Mhdawi MKS, Brito M, Onggo BS, et al. COVID-19 emerging risk assessment for the construction industry of developing countries: evidence from Iraq. International Journal of Construction Management. Published online January 23, 2023: 1-14. doi: 10.1080/15623599.2023.2169301

[164] Gashaw T, Jilcha K. Design risk modeling and analysis for railway construction projects. International Journal of Construction Management. 2022; 23(14): 2488-2498. doi: 10.1080/15623599.2022.2070344

[165] Do ST, Nguyen VT, Likhitruangsilp V. RSIAM risk profile for managing risk factors of international construction joint ventures. International Journal of Construction Management. 2021; 23(7): 1148-1162. doi: 10.1080/15623599.2021.1957753

[166] He S, Xu H, Zhang J, et al. Risk assessment of oil and gas pipelines hot work based on AHP-FCE. Petroleum. 2023; 9(1): 94-100. doi: 10.1016/j.petlm.2022.03.006

[167] Mohandes SR, Durdyev S, Sadeghi H, et al. Towards enhancement in reliability and safety of construction projects: developing a hybrid multi-dimensional fuzzy-based approach. Engineering, Construction and Architectural Management. 2022; 30(6): 2255-2279. doi: 10.1108/ecam-09-2021-0817

[168] Badi I, Bouraima MB, Jibril ML. Risk Assessment in Construction Projects Using the Grey Theory. Journal of Engineering Management and Systems Engineering. 2022; 1(2): 58-66. doi: 10.56578/jemse010203

[169] Zhang L, Li H. Construction Risk Assessment of Deep Foundation Pit Projects Based on the Projection Pursuit Method and Improved Set Pair Analysis. Applied Sciences. 2022; 12(4): 1922. doi: 10.3390/app12041922

[170] Ju W, Wu J, Kang Q, et al. A method based on the theories of game and extension cloud for risk assessment of construction safety: A case study considering disaster-inducing factors in the construction process. Journal of Building Engineering. 2022; 62: 105317. doi: 10.1016/j.jobe.2022.105317

[171] Sadeghi M, Mahmoudi A, Deng X. Blockchain technology in construction organizations: risk assessment using trapezoidal fuzzy ordinal priority approach. Engineering, Construction and Architectural Management. 2022; 30(7): 2767-2793. doi: 10.1108/ecam-01-2022-0014

Published
2024-04-26
How to Cite
Junjia, Y., Alias, A. H., Haron, N. A., & Abu Bakar, N. (2024). Machine learning algorithms for safer construction sites: Critical review. Building Engineering, 2(1), 544. https://doi.org/10.59400/be.v2i1.544
Section
Review