A Classification Model Using Personal Biometric Characteristics to Identify Individuals Vulnerable to an Extremely Hot Environment
Publication: Journal of Management in Engineering
Volume 40, Issue 2
Abstract
The rise in heatwaves due to climate change is becoming a significant concern for outdoor workers, particularly leading to an increasing number of heat-related illnesses. To address the challenge, this study aimed to propose, as a process-based approach, a classification model using personal biometric characteristics to identify individuals who are vulnerable to extremely hot environments (i.e., high-risk groups). To this end, an experimental study was conducted, and experimental conditions were set in an environmental chamber by considering the extremely hot summer weather in Korea. With the data collected from a total of 70 people who voluntarily participated in the experiment, the classification model was developed by adopting multiple methodologies such as time-series clustering, independent samples t-test, and machine-learning algorithms. Consequently, it was found that the classification performance was the best with the multilayer perceptron algorithm, resulting in 0.800 in terms of the area under the receiver operating characteristic (AUROC) and 0.811 in terms of the area under the precision-recall curve (AUPRC). This study creates new ground in identifying individuals vulnerable to extremely hot environments in the domain of management in engineering by employing machine-learning-based classification algorithms with personal biometric characteristics. The proposed approach can be realized by utilizing a simple and low-cost bioelectrical impedance method for estimating human body composition (such as body fat mass and skeletal muscle mass) before they are put into the field. It is expected to aid in providing a more systematic and individualized management system for proactively preventing personal heat-related illnesses.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This work was supported by the Incheon National University Research Grant in 2021 (No. 2021-0023).
References
Aboseif, E., and A. S. Hanna. 2023. “Defining the success status of construction projects based on quantitative performance metrics thresholds.” J. Manage. Eng. 39 (2): 04022073. https://doi.org/10.1061/JMENEA.MEENG-5123.
Acharya, P., B. Boggess, and K. Zhang. 2018. “Assessing heat stress and health among construction workers in a changing climate: A review.” Int. J. Environ. Res. Public Health 15 (2): 247. https://doi.org/10.3390/ijerph15020247.
Ahmed, M. O., and I. H. El-adaway. 2023. “Data-driven analysis of construction bidding stage–related causes of disputes.” J. Manage. Eng. 39 (5): 04023026. https://doi.org/10.1061/JMENEA.MEENG-5426.
Albert, A., and J. Anderson. 1984. “On the existence of maximum likelihood estimates in logistic regression models.” Biometrika 71 (1): 1–10. https://doi.org/10.1093/biomet/71.1.1.
Ashari, A., I. Paryudi, and A. M. Tjoa. 2013. “Performance comparison between Naïve Bayes, decision tree and k-nearest neighbor in searching alternative design in an energy simulation tool.” Int. J. Adv. Comput. Sci. Appl. 4 (11): 33–39. https://doi.org/10.14569/IJACSA.2013.041105.
Assaad, R., and I. H. El-adaway. 2020. “Evaluation and prediction of the hazard potential level of dam infrastructures using computational artificial intelligence algorithms.” J. Manage. Eng. 36 (5): 04020051. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000810.
Assaad, R., and I. H. El-adaway. 2021. “Impact of dynamic workforce and workplace variables on the productivity of the construction industry: New gross construction productivity indicator.” J. Manage. Eng. 37 (1): 04020092. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000862.
Awada, M., F. J. Srour, and I. M. Srour. 2020. “Data-driven machine learning approach to integrate field submittals in project scheduling.” J. Manage. Eng. 37 (1): 04020104. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000873.
Baccini, M., et al. 2008. “Heat effects on mortality in 15 European cities.” Epidemiology 19 (5): 711–719. https://doi.org/10.1097/EDE.0b013e318176bfcd.
Bai, L., G. Ding, S. Gu, P. Bi, B. Su, D. Qin, G. Xu, and Q. Liu. 2014. “The effects of summer temperature and heatwaves on heat-related illness in a coastal city of China, 2011–2013.” Environ. Res. 132 (Jul): 212–219. https://doi.org/10.1016/j.envres.2014.04.002.
Bashir, A., and S. Kivrak. 2020. “Investigating engineer’s actual usage intention to apply construction technologies in emerging countries.” J. Manage. Eng. 8 (3): 80–89. https://doi.org/10.21595/jme.2020.21167.
Basu, R., and B. Ostro. 2008. “A multicounty analysis identifying the populations vulnerable to mortality associated with high ambient temperature in California.” Am. J. Epidemiol. 168 (6): 632–637. https://doi.org/10.1093/aje/kwn170.
Bell, M., M. O’Neill, N. Ranjit, V. Borja-Aburto, L. Cifuentes, and N. Gouveia. 2008. “Vulnerability to heat-related mortality in Latin America: A case-crossover study in São Paulo, Brazil, Santiago, Chile and Mexico City, Mexico.” Int. J. Epidemiol. 37 (4): 796–804. https://doi.org/10.1093/ije/dyn094.
Bholowalia, P., and A. Kumar. 2014. “EBK-means: A clustering technique based on elbow method and k-means in WSN.” Int. J. Comput. Appl. 105 (9): 17–24. https://doi.org/10.5120/18405-9674.
Boateng, E., and D. Abaye. 2019. “A review of the logistic regression model with emphasis on medical research.” J. Data Anal. Inf. Process. 7 (4): 190–207. https://doi.org/10.4236/jdaip.2019.74012.
Breiman, L. 2001. “Random forests.” Mach. Learn. 45 (Oct): 5–32. https://doi.org/10.1023/A:1010933404324.
Buller, M., W. Tharion, C. Duhamel, and M. Yokota. 2015. “Real-time core body temperature estimation from heart rate for first responders wearing different levels of personal protective equipment.” Ergonomics 58 (11): 1830–1841. https://doi.org/10.1080/00140139.2015.1036792.
Chan, A., M. Yam, J. Chung, and W. Yi. 2012. “Developing a heat stress model for construction workers.” J. Facil. Manage. 10 (1): 59–74. https://doi.org/10.1108/14725961211200405.
Chan, A. P. C., W. Yi, D. W. M. Chan, and D. P. Wong. 2013. “Using the thermal work limit as an environmental determinant of heat stress for construction workers.” J. Manage. Eng. 29 (4): 414–423. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000162.
Chang, T., S. Yoon, S. Chi, and S. Im. 2023. “Condition estimation of facility elements using XGBoost.” Korean J. Constr. Eng. Manage. 24 (1): 31–39. https://doi.org/10.6106/KJCEM.2023.24.1.031.
Chen, J., H. L. Chi, Q. Du, and P. Wu. 2022. “Investigation of operational concerns of construction crane operators: An approach integrating factor clustering and prioritization.” J. Manage. Eng. 38 (4): 04022020. https://doi.org/10.1061/(ASCE)ME.1943-5479.0001044.
Chen, Q., D. Long, C. Yang, and H. Xu. 2023. “Knowledge graph improved dynamic risk analysis method for behavior-based safety management on a construction site.” J. Manage. Eng. 39 (4): 04023023. https://doi.org/10.1061/JMENEA.MEENG-5306.
Chen, T., and C. Guestrin. 2016. “XGBoost: A scalable tree boosting system.” In Proc., 22nd ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, 785–794. New York: Association for Computing Machinery.
Chiu, S., L. Fan, C. Lin, T. Chen, W. Lim, J. Jang, and M. Chiu. 2022. “Machine learning-based classification of subjective cognitive decline, mild cognitive impairment, and Alzheimer’s dementia using neuroimage and plasma biomarkers.” ACS Chem. Neurosci. 13 (23): 3263–3270. https://doi.org/10.1021/acschemneuro.2c00255.
Choi, J., D. P. de Oliveira, and F. Leite. 2022. “A novel approach to capture similarity in capital project benchmarking: An application to healthcare projects.” J. Manage. Eng. 38 (3): 05022007. https://doi.org/10.1061/(ASCE)ME.1943-5479.0001039.
Choi, J., V. Loftness, and D. Lee. 2012. “Investigation of the possibility of the use of heart rate as a human factor for thermal sensation models.” Build. Environ. 50 (Apr): 165–175. https://doi.org/10.1016/j.buildenv.2011.10.009.
Cortes, C., and V. Vapnik. 1995. “Support-vector networks.” Mach. Learn. 20 (Sep): 273–297. https://doi.org/10.1007/BF00994018.
Cui, S., L. Zhao, Y. Wang, Q. Dong, J. Ma, Y. Wang, W. Zhao, and X. Ma. 2018. “Using Naive Bayes classifier to predict osteonecrosis of the femoral head with cannulated screw fixation.” Injury 49 (10): 1865–1870. https://doi.org/10.1016/j.injury.2018.07.025.
Davis, J., and M. Goadrich. 2006. “The relationship between precision-recall and ROC curves.” In Proc., 23rd Int. Conf. on Machine Learning, 233–240. New York: Association for Computing Machinery.
Dehghan, H., S. Mortazavi, M. Jafari, and M. Maracy. 2012. “Evaluation of wet bulb globe temperature index for estimation of heat strain in hot/humid conditions in the Persian Gulf.” J. Res. Med. Sci. 17 (12): 1108–1113.
Desdhanty, V., and Z. Rustam. 2021. “Liver cancer classification using random forest and extreme gradient boosting (XGBoost) with genetic algorithm as feature selection.” In Proc., 2021 Int. Conf. on Decision Aid Sciences and Application (DASA), 716–719. New York: IEEE.
Du, C., B. Li, Y. Li, M. Xu, and R. Yao. 2019. “Modification of the predicted heat strain (PHS) model in predicting human thermal responses for Chinese workers in hot environments.” Build. Environ. 165 (Nov): 106349. https://doi.org/10.1016/j.buildenv.2019.106349.
Frias-Martinez, E., A. Sanchez, and J. Velez. 2006. “Support vector machines versus multi-layer perceptrons for efficient off-line signature recognition.” Eng. Appl. Artif. Intell. 19 (6): 693–704. https://doi.org/10.1016/j.engappai.2005.12.006.
Gubernot, D., G. Anderson, and K. Hunting. 2015. “The epidemiology of occupational heat exposure in the United States: A review of the literature and assessment of research needs in a changing climate.” Int. J. Biometeorol. 58 (8): 1779–1788. https://doi.org/10.1007/s00484-013-0752-x.
Hajat, S., R. Kovats, and K. Lachowycz. 2007. “Heat-related and cold-related deaths in England and Wales: Who is at risk?” Occup. Environ. Med. 64 (2): 93–100. https://doi.org/10.1136/oem.2006.029017.
Han, J., M. Kamber, and J. Pei. 2012. Data mining concepts and techniques. 3rd ed. Cambridge, MA: Elsevier.
Hastie, T., R. Tibshirani, J. H. Friedman, and J. H. Friedman. 2009. Vol. 2 of The elements of statistical learning: Data mining, inference, and prediction. 2nd ed. Washington, DC: International Biometric Society.
Ingwersen, E., et al. 2023. “Machine learning versus logistic regression for the prediction of complications after pancreatoduodenectomy.” Surgery 17 (3): 435–440. https://doi.org/10.1016/j.surg.2023.03.012.
IPCC (Intergovernmental Panel on Climate Change). 2022. Climate change 2022: Impacts, adaptation and vulnerability. Geneva: IPCC.
Ishigami, A., S. Hajat, R. Kovats, L. Bisanti, M. Rognoni, A. Russo, and A. Paldy. 2008. “An ecological time-series study of heat-related mortality in three European cities.” Environ. Health 7 (1): 1–7. https://doi.org/10.1186/1476-069X-7-5.
ISO. 2004a. Ergonomics: Evaluation of thermal strain by physiological measurement. ISO 9886:2004. Geneva: ISO.
ISO. 2004b. Ergonomics of the thermal environment: Analytical determination and interpretation of heat stress using calculation of the predicted heat strain. ISO 7933:2004. Geneva: ISO.
ISO. 2004c. Ergonomics of the thermal environment: Determination of metabolic rate. ISO 8996:2004. Geneva: ISO.
ISO. 2017. Ergonomics of the thermal environment—Assessment of heat stress using the WBGT (wet bulb globe temperature) index. ISO 7243:2017. Geneva: ISO.
Jain, A., F. Soojin Yoon, K. Kang, and M. Hastak. 2022. “Implementation of the data-driven analytics protocol through facility management and real estate industry cases.” J. Manage. Eng. 38 (1): 04021077. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000894.
Jeong, J., and J. Jeong. 2022. “Quantitative risk evaluation of fatal incidents in construction based on frequency and probability analysis.” J. Manage. Eng. 38 (2): 04021089. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000998.
Jiang, Q., M. Qiang, and C. Lin. 2022. “Collaborative evaluation method of multiple team membership: Case study of engineering design company.” J. Manage. Eng. 38 (6): 05022014. https://doi.org/10.1061/(ASCE)ME.1943-5479.0001096.
Kakamu, T., S. Endo, T. Hidaka, Y. Masuishi, H. Kasuga, and T. Fukushima. 2021. “Heat-related illness risk and associated personal and environmental factors of construction workers during work in summer.” Sci. Rep. 11 (1): 1119. https://doi.org/10.1038/s41598-020-79876-w.
Kamardeen, I., and A. Hasan. 2023. “Analysis of work-related psychological injury severity among construction trades workers.” J. Manage. Eng. 39 (2): 04023001. https://doi.org/10.1061/JMENEA.MEENG-5041.
Kanungo, T., D. M. Mount, N. S. Netanyahu, C. D. Piatko, R. Silverman, and A. Y. Wu. 2022. “An efficient k-means clustering algorithm: Analysis and implementation.” IEEE Trans. Pattern Anal. Mach. Intell. 24 (7): 881–892. https://doi.org/10.1109/TPAMI.2002.1017616.
Karaca, I., D. D. Gransberg, and H. D. Jeong. 2020. “Improving the accuracy of early cost estimates on transportation infrastructure project.” J. Manage. Eng. 36 (5): 04020063. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000819.
Karimzadeh, A., S. Sabeti, and O. Shoghli. 2021. “Optimal clustering of pavement segments using K-prototype algorithm in a high-dimensional mixed feature space.” J. Manage. Eng. 37 (4): 04021022. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000910.
Karthick, S., S. Kermanshachi, and A. Pamidimukkala. 2023. “Analysis of the health and safety challenges faced by construction workers in extremely hot weather conditions.” J. Leg. Aff. Dispute Resolut. Eng. Constr. 15 (1): 04522048. https://doi.org/10.1061/JLADAH.LADR-882.
Karthick, S., S. Kermanshachi, and I. Ramaji. 2022. “Health and safety of construction field workforce active in extreme weather conditions.” In Proc., Construction Research Congress 2022, 737–747. Reston, VA: ASCE.
Kazmierska, J., and J. Malicki. 2008. “Application of the Naïve Bayesian classifier to optimize treatment decisions.” Radiother. Oncol. 86 (2): 211–216. https://doi.org/10.1016/j.radonc.2007.10.019.
Khalef, R., and I. H. El-adaway. 2021. “Automated identification of substantial changes in construction projects of airport improvement program: Machine learning and natural language processing comparative analysis.” J. Manage. Eng. 37 (6): 04021062. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000959.
Kim, H., M. Jung, D. Oh, H. Cho, S. Choi, H. Jang, and J. Kim. 2022. “Indoor temperature analysis by point according to facility operation of IOT-based vertical smart farm.” Korean J. Constr. Eng. Manage. 23 (1): 98–105. https://doi.org/10.6106/KJCEM.2022.23.1.098.
Kim, K., J. Lim, C. Yang, S. Jang, and S. Lee. 2021. “Classification of adolescent psychiatric patients at high risk of suicide using the personality assessment inventory by machine learning.” Psychiatry Invest. 18 (11): 1137. https://doi.org/10.30773/pi.2021.0191.
King, M., and P. Resick. 2014. “Data mining in psychological treatment research: A primer on classification and regression trees.” J. Consulting Clin. Psychol. 82 (5): 895. https://doi.org/10.1037/a0035886.
Kjellstrom, T., N. Maître, C. Saget, M. Otto, and T. Karimova. 2019. Working on a warmer planet: The effect of heat stress on productivity and decent work. Geneva: International Labour Office.
Koc, K. 2023a. “Role of national conditions in occupational fatal accidents in the construction industry using interpretable machine learning approach.” J. Manage. Eng. 39 (6): 04023037. https://doi.org/10.1061/JMENEA.MEENG-5516.
Koc, K. 2023b. “Role of Shapley additive explanations and resampling algorithms for contract failure prediction of public–private partnership projects.” J. Manage. Eng. 39 (5): 04023031. https://doi.org/10.1061/JMENEA.MEENG-5492.
KOSHA (Korea Occupational Safety and Health Agency). 2015a. Monthly safety and health. Ulsan, South Korea: KOSHA.
KOSHA (Korea Occupational Safety and Health Agency). 2015b. A study on the legal basis and implementation plan for measures to improve working conditions, such as rest from harmful work (focusing on outdoor work). Ulsan, South Korea: KOSHA.
Kwak, S., and J. Kim. 2012. “Statistical data preparation: Management of missing values and outliers.” Korean J. Anesthesiol. 70 (4): 407–411. https://doi.org/10.4097/kjae.2017.70.4.407.
Kwon, S., and M. Park. 2020. “Time-series data clustering based on the correlation of periodogram.” J. Korean Data Anal. Soc. 22 (5): 1751–1766. https://doi.org/10.37727/jkdas.2020.22.5.1751.
Law, K. K., S. Chang, and M. F. F. Siu. 2022. “Factors influencing adoption of construction robotics in Hong Kong’s industry: A multistakeholder perspective.” J. Manage. Eng. 38 (2): 04021096. https://doi.org/10.1061/(ASCE)ME.1943-5479.0001011.
Lee, J., S. Kim, and M. Lee. 2023. “A study on the development of big data-based AI water meter freeze and burst risk information service.” Korean J. Constr. Eng. Manage. 24 (3): 42–51. https://doi.org/10.6106/KJCEM.2023.24.3.042.
Lee, J., T. W. Kim, and C. Koo. 2022a. “A novel process model for developing a scalable room-level energy benchmark using real-time bigdata: Focused on identifying representative energy usage patterns.” Renewable Sustainable Energy Rev. 169 (Nov): 112944. https://doi.org/10.1016/j.rser.2022.112944.
Lee, J., J. Lee, T. W. Kim, and C. Koo. 2022b. “EEG-based circumplex model of affect for identifying interindividual differences in thermal comfort.” J. Manage. Eng. 38 (4): 04022034. https://doi.org/10.1061/(ASCE)ME.1943-5479.0001061.
Lever, J. 2016. “Classification evaluation: It is important to understand both what a classification metric expresses and what it hides.” Nat. Methods 13 (8): 603–604. https://doi.org/10.1038/nmeth.3945.
Li, Y., H. Sun, D. Li, J. Song, and R. Ding. 2022. “Effects of digital technology adoption on sustainability performance in construction projects: The mediating role of stakeholder collaboration.” J. Manage. Eng. 38 (3): 04022016. https://doi.org/10.1061/(ASCE)ME.1943-5479.0001040.
Liao, L., N. Wei, C. Zheng, Y. Ye, and Y. Chen. 2023. “Understanding causes for construction claims in hydropower projects.” J. Manage. Eng. 39 (5): 04023032. https://doi.org/10.1061/JMENEA.MEENG-5247.
Lim, J., S. Han, Y. Kang, and S. Kang. 2021. “Affinity analysis between factors of fatal occupational accidents in construction using data mining techniques.” Korean J. Constr. Eng. Manage. 22 (5): 29–38. https://doi.org/10.6106/KJCEM.2021.22.5.029.
Lin, M., A. Ali, M. S. Andargie, and E. Azar. 2021. “Multidomain drivers of occupant comfort, productivity, and well-being in buildings: Insights from an exploratory and explanatory analysis.” J. Manage. Eng. 37 (4): 04021020. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000923.
Martino, A., A. Rizzi, and F. M. F. Mascioli. 2018. “Distance matrix pre-caching and distributed computation of internal validation indices in k-medoids clustering.” In Proc., 2018 Int. Joint Conf. on Neural Networks (IJCNN), 1–8. New York: IEEE.
Mughnyanti, M., S. Efendi, and M. Zarlis. 2020. “Analysis of determining centroid clustering x-means algorithm with Davies-Bouldin index evaluation.” IOP Conf. Ser.: Mater. Sci. Eng. 725 (1): 012128. https://doi.org/10.1088/1757-899X/725/1/012128.
Muller, M. P., G. Tomlinson, T. J. Marrie, P. Tang, A. McGeer, D. E. Low, A. D. Detsky, and W. L. Gold. 2005. “Can routine laboratory tests discriminate between severe acute respiratory syndrome and other causes of community-acquired pneumonia?” Clin. Infect. Dis. 40 (8): 1079–1086. https://doi.org/10.1086/428577.
Na, S., L. Xumin, and G. Yong. 2010. “Research on k-means clustering algorithm: An improved k-means clustering algorithm.” In Proc., 2010 3rd Int. Symp. on Intelligent Information Technology and Security Informatics, 63–67. New York: IEEE.
Naji, K. K., M. Gunduz, and A. F. Naser. 2022. “An adaptive neurofuzzy inference system for the assessment of change order management performance in construction.” J. Manage. Eng. 38 (2): 04021098. https://doi.org/10.1061/(ASCE)ME.1943-5479.0001017.
Nayak, A., and D. Natarajan. 2016. “Comparative study of naive Bayes, support vector machine and random forest classifiers in sentiment analysis of Twitter feeds.” Int. J. Adv. Stud. Comput. Sci. Eng. 5 (1): 16.
Nazarian, N., et al. 2021. “Project Coolbit: Can your watch predict heat stress and thermal comfort sensation?” Environ. Res. Lett. 16 (3): 034031. https://doi.org/10.1088/1748-9326/abd130.
Nwaogu, J. M., A. P. Chan, and J. A. Naslund. 2022. “Measures to improve the mental health of construction personnel based on expert opinions.” J. Manage. Eng. 38 (4): 04022019. https://doi.org/10.1061/(ASCE)ME.1943-5479.0001045.
Padierna, L., L. Amador-Medina, B. Murillo-Ortiz, and C. Villaseñor-Mora. 2020. “Classification method of peripheral arterial disease in patients with type 2 diabetes mellitus by infrared thermography and machine learning.” Infrared Phys. Technol. 111 (Dec): 103531. https://doi.org/10.1016/j.infrared.2020.103531.
Pantavou, K., S. Lykoudis, and G. Nikolopoulos. 2016. “Milder form of heat-related symptoms and thermal sensation: A study in a Mediterranean climate.” Int. J. Biometeorol. 60 (6): 917–929. https://doi.org/10.1007/s00484-015-1085-8.
Patel, D. A., and K. N. Jha. 2015. “Neural network approach for safety climate prediction.” J. Manage. Eng. 31 (6): 05014027. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000348.
Qi, Q., Y. Luo, Z. Xu, S. Ji, and T. Yang. 2021. “Stochastic optimization of areas under precision-recall curves with provable convergence.” Preprint, submitted on April 18, 2021. https://doi.org/10.48550/arXiv.2104.08736.
Rastogi, S., B. Gupta, N. Mathnr, and T. Husain. 1989. “Thermal stress and physiological strain of children exposed to hot environments in a glass bangle factory.” Eur. J. Appl. Physiol. Occup. Physiol. 59 (4): 290–295. https://doi.org/10.1007/BF02388331.
Rehman, R. Z. U., S. Del Din, J. Q. Shi, B. Galna, S. Lord, A. J. Yarnall, Y. Guan, and L. Rochester. 2019. “Comparison of walking protocols and gait assessment systems for machine learning-based classification of Parkinson’s disease.” Sensors 19 (24): 5363. https://doi.org/10.3390/s19245363.
Salmi, N., and Z. Rustam. 2010. “Naïve Bayes classifier models for predicting the colon cancer.” IOP Conf. Ser.: Mater. Sci. Eng. 546 (5): 052068. https://doi.org/10.1088/1757-899X/546/5/052068.
Su, Y., C. Mao, R. Jiang, G. Liu, and J. Wang. 2021. “Data-driven fire safety management at building construction sites: Leveraging CNN.” J. Manage. Eng. 37 (2): 04020108. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000877.
Sun, R., K. Hsieh, and J. Sosnoff. 2019. “Fall risk prediction in multiple sclerosis using postural sway measures: A machine learning approach.” Sci. Rep. 9 (1): 16154. https://doi.org/10.1038/s41598-019-52697-2.
Syakur, M., B. Khotimah, E. Rochman, and B. Satoto. 2018. “Integration k-means clustering method and elbow method for identification of the best customer profile cluster.” IOP Conf. Ser.: Mater. Sci. Eng. 336 (Nov): 012017. https://doi.org/10.1088/1757-899X/336/1/012017.
Umar, T., and C. Egbu. 2020. “Heat stress, a hidden cause of accidents in construction.” Proc. Inst. Civ. Eng. Munic. Eng. 173 (1): 49–60. https://doi.org/10.1680/jmuen.18.00004.
Vaneckova, P., M. A. Hart, P. J. Beggs, and R. J. Dear. 2008. “Synoptic analysis of heat-related mortality in Sydney, Australia, 1993–2001.” Int. J. Biometeorol. 52 (6): 439–451. https://doi.org/10.1007/s00484-007-0138-z.
Wang, R., C. J. Lee, S. C. Hsu, and C. Y. Lee. 2018. “Corporate misconduct prediction with support vector machine in the construction industry.” J. Manage. Eng. 34 (4): 04018021. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000630.
Wang, X., C. Liu, X. Song, and X. Cui. 2022. “Development of an internet-of-things-based technology system for construction safety hazard prevention.” J. Manage. Eng. 38 (3): 04022009. https://doi.org/10.1061/(ASCE)ME.1943-5479.0001035.
Wang, Y., and R. L. Tiong. 2022. “Public–private partnership contract failure prediction using example-dependent cost-sensitive models.” J. Manage. Eng. 38 (1): 04021079. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000990.
Watts, N., et al. 2019. “The 2019 report of The Lancet Countdown on health and climate change: Ensuring that the health of a child born today is not defined by a changing climate.” Lancet 394 (10211): 1836–1878. https://doi.org/10.1016/S0140-6736(19)32596-6.
WHO (World Health Organization). 1969. Health factors involved in working under conditions of heat stress. Geneva: WHO.
Wu, H., B. Zhong, H. Li, J. Guo, and Y. Wang. 2021. “On-site construction quality inspection using Blockchain and smart contracts.” J. Manage. Eng. 37 (6): 04021065. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000967.
Xiang, J., P. Bi, D. Pisaniello, and A. Hansen. 2014. “The impact of heatwaves on workers’ health and safety in Adelaide, South Australia.” Environ. Res. 133 (Aug): 90–95. https://doi.org/10.1016/j.envres.2014.04.042.
Yang, J., Y. Chen, H. Yao, and B. Zhang. 2022. “Machine learning–driven model to analyze particular conditions of contracts: A multifunctional and risk perspective.” J. Manage. Eng. 38 (5): 04022036. https://doi.org/10.1061/(ASCE)ME.1943-5479.0001068.
Yao, R., Y. Li, C. Du, and B. Li. 2018. “A ‘heart rate’-based model (PHSHR) for predicting personal heat stress in dynamic working environments.” Build. Environ. 135 (May): 318–329. https://doi.org/10.1016/j.buildenv.2018.03.014.
Yi, W., and A. P. C. Chan. 2015. “Which environmental indicator is better able to predict the effects of heat stress on construction workers?” J. Manage. Eng. 31 (4): 04014063. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000284.
Yip, F., W. Flanders, A. Wolkin, D. Engelthaler, W. Humble, A. Neri, L. Lewis, L. Backer, and C. Rubin. 2008. “The impact of excess heat events in Maricopa County, Arizona: 2000–2005.” Int. J. Biometeorol. 52 (8): 765–772. https://doi.org/10.1007/s00484-008-0169-0.
Yu, C. Y., K. C. Lam, and P. Yung. 2014. “Factors that influence the concession period length for tunnel projects under BOT contracts.” J. Manage. Eng. 30 (1): 108–121. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000180.
Zare, S., H. Shirvan, R. Hemmatjo, F. Nadri, Y. Jahani, K. Jamshidzadeh, and P. Paydar. 2019. “A comparison of the correlation between heat stress indices (UTCI, WBGT, WBDT, TSI) and physiological parameters of workers in Iran.” Weather Clim. Extremes 26 (Dec): 100213. https://doi.org/10.1016/j.wace.2019.100213.
Zhang, J., J. C. Cheng, W. Chen, and K. Chen. 2022. “Digital twins for construction sites: Concepts, LoD definition, and applications.” J. Manage. Eng. 38 (2): 04021094. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000948.
Zhang, L., O. Ma, J. M. Fabre, R. H. Wood, S. U. Garcia, K. M. Ivey, and E. D. McCann. 2015. “Classification of older adults with/without a fall history using machine learning methods.” In Proc., 2015 37th Annual Int. Conf. of the IEEE Engineering in Medicine and Biology Society (EMBC), 6760–6763. New York: IEEE.
Zhang, S., R. Y. Sunindijo, and M. Loosemore. 2023a. “The influence of personal and workplace characteristics on the job stressors of design professionals in the Chinese construction industry.” J. Manage. Eng. 39 (5): 05023006. https://doi.org/10.1061/JMENEA.MEENG-5303.
Zhang, X., F. Chen, and Z. Chen. 2023b. “Heatwave and mental health.” J. Environ. Manage. 332 (Apr): 117385. https://doi.org/10.1016/j.jenvman.2023.117385.
Zhou, Y., L. Wang, Y. Xu, L. Ding, and Z. Tang. 2022. “Intelligent Fangcang shelter hospital systems for major public health emergencies: The case of the Optics Valley Fangcang Shelter Hospital.” J. Manage. Eng. 38 (1): 05021010. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000976.
Information & Authors
Information
Published In
Journal of Management in Engineering
Volume 40 • Issue 2 • March 2024
Copyright
© 2024 American Society of Civil Engineers.
History
Received: Feb 1, 2023
Accepted: Oct 19, 2023
Published online: Jan 8, 2024
Published in print: Mar 1, 2024
Discussion open until: Jun 8, 2024
ASCE Technical Topics:
- Algorithms
- Artificial intelligence and machine learning
- Business management
- Climate change
- Climates
- Computer programming
- Computing in civil engineering
- Data collection
- Diseases
- Employment
- Engineering fundamentals
- Environmental engineering
- Health hazards
- Labor
- Mathematics
- Methodology (by type)
- Occupational safety
- Personnel management
- Practice and Profession
- Public administration
- Public health and safety
- Research methods (by type)
- Safety
- Systems engineering
- Systems management
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.