Enhancing construction safety: Machine learning-based classification of injury types

[1]  R. Parthiban,et al.  Predicting individual learning performance using machine‐learning hybridized with the teaching‐learning‐based optimization , 2022, Comput. Appl. Eng. Educ..

[2]  A. Yoshino,et al.  Differences in Trauma Injury Patterns and Severity Between Intentional and Accidental Falls From a Height: A Japanese Nationwide Trauma Database Study , 2022, Cureus.

[3]  Heng Li,et al.  Worksite Accident Impacts on Construction and Infrastructure: Nondeterministic Analysis of Subsectors and Organization Sizes , 2022, Journal of Construction Engineering and Management.

[4]  V. Kamat,et al.  Computer vision for anatomical analysis of equipment in civil infrastructure projects: Theorizing the development of regression-based deep neural networks , 2022, Automation in Construction.

[5]  Chao Wu,et al.  Incremental semi-supervised Extreme Learning Machine for Mixed data stream classification , 2021, Expert Syst. Appl..

[6]  V. Leggieri,et al.  Machine-learning based vulnerability analysis of existing buildings , 2021, Automation in Construction.

[7]  Patrick X.W. Zou,et al.  Discovery of new safety knowledge from mining large injury dataset in construction , 2021, Safety Science.

[8]  Youngsang Kwon,et al.  Leveraging machine learning to understand urban change with net construction , 2021, Landscape and Urban Planning.

[9]  M. Arashpour,et al.  The Cost Burden of Safety Risk Incidents on Construction: A Probabilistic Quantification Method , 2021, Risk analysis : an official publication of the Society for Risk Analysis.

[10]  S. R. Mohandes,et al.  Simulation-based analysis of occupational health and safety continuous improvement (OHSCI) in modern construction and infrastructure industries , 2021, Automation in Construction.

[11]  Jingluan Wang,et al.  Risk assessment for musculoskeletal disorders based on the characteristics of work posture , 2021 .

[12]  Nipesh Pradhananga,et al.  Sensor-based computational approach to preventing back injuries in construction workers , 2021, Automation in Construction.

[13]  Mani Golparvar-Fard,et al.  Machine Learning-Based Risk Analysis for Construction Worker Safety from Ubiquitous Site Photos and Videos , 2021 .

[14]  Asli Pelin Gurgun,et al.  Integrating feature engineering, genetic algorithm and tree-based machine learning methods to predict the post-accident disability status of construction workers , 2021 .

[15]  N. Berry,et al.  Machine learning - Predicting Ames mutagenicity of small molecules. , 2021, Journal of molecular graphics & modelling.

[16]  M. Z. Naser,et al.  An engineer's guide to eXplainable Artificial Intelligence and Interpretable Machine Learning: Navigating causality, forced goodness, and the false perception of inference , 2021 .

[17]  Odey Alshboul,et al.  Machine learning models for predicting the residual value of heavy construction equipment: An evaluation of modified decision tree, LightGBM, and XGBoost regression , 2021 .

[18]  S. Khanna,et al.  A Robust Machine Learning Based Framework for the Automated Detection of ADHD Using Pupillometric Biomarkers and Time Series Analysis , 2021, Scientific Reports.

[19]  Maxwell Fordjour Antwi-Afari,et al.  Effects of load carrying techniques on gait parameters, dynamic balance, and physiological parameters during a manual material handling task , 2021, Engineering, Construction and Architectural Management.

[20]  Tao Lu,et al.  A novel methodology for the path alignment of visual SLAM in indoor construction inspection , 2021, Automation in Construction.

[21]  Heng Li,et al.  Quantitative Analysis of Safety Risks and Relationship with Delayed Project Completion Times , 2021, Risk analysis : an official publication of the Society for Risk Analysis.

[22]  Yong Han Ahn,et al.  Developing a machine learning-based building repair time estimation model considering weight assigning methods , 2021 .

[23]  K. Brolin,et al.  Work-related traumatic brain injury in the construction industry in Sweden and Germany , 2021 .

[24]  Collin Barker,et al.  Real-Time Nuisance Fault Detection in Photovoltaic Generation Systems Using a Fine Tree Classifier , 2021, Sustainability.

[25]  Shui-Long Shen,et al.  Risk assessment and management of excavation system based on fuzzy set theory and machine learning methods , 2021 .

[26]  K. Oh,et al.  Machine learning model to predict hypotension after starting continuous renal replacement therapy , 2020, Scientific Reports.

[27]  Zhaoyi Xu,et al.  Machine Learning for Reliability Engineering and Safety Applications: Review of Current Status and Future Opportunities , 2020, Reliab. Eng. Syst. Saf..

[28]  Li Ying,et al.  Detecting safety helmet wearing on construction sites with bounding‐box regression and deep transfer learning , 2020, Comput. Aided Civ. Infrastructure Eng..

[29]  Felix Abramovich,et al.  Multiclass Classification by Sparse Multinomial Logistic Regression , 2020, IEEE Transactions on Information Theory.

[30]  Xiaofeng Hu,et al.  Application of machine learning techniques for predicting the consequences of construction accidents in China , 2021, Process Safety and Environmental Protection.

[31]  Mehrdad Arashpour,et al.  Scene understanding in construction and buildings using image processing methods: A comprehensive review and a case study , 2021 .

[32]  Mehrdad Arashpour,et al.  Safety management in construction: 20 years of risk modeling , 2020 .

[33]  K. Musselman,et al.  Exploring the causes and impacts of falls among ambulators with spinal cord injury using photovoice: a mixed-methods study , 2020, BMJ Open.

[34]  André Dorée,et al.  Maintenance intervention predictions using entity-embedding neural networks , 2020, Automation in Construction.

[35]  M. Kattan,et al.  A Framework for the Evaluation of Statistical Prediction Models. , 2020, Chest.

[36]  Zelong Liu,et al.  A sound monitoring system for prevention of underground pipeline damage caused by construction , 2020 .

[37]  Jhareswar Maiti,et al.  Predicting and analyzing injury severity: A machine learning-based approach using class-imbalanced proactive and reactive data , 2020 .

[38]  Huiling Chen,et al.  Predicting Cervical Hyperextension Injury: A Covariance Guided Sine Cosine Support Vector Machine , 2020, IEEE Access.

[39]  Sangyoon Chin,et al.  Machine learning predictive model based on national data for fatal accidents of construction workers , 2020 .

[40]  Dmitry Koroteev,et al.  Application of machine learning to accidents detection at directional drilling , 2019, Journal of Petroleum Science and Engineering.

[41]  Lukumon O. Oyedele,et al.  Guidelines for applied machine learning in construction industry - A case of profit margins estimation , 2020, Adv. Eng. Informatics.

[42]  Amin Heidarpour,et al.  Performance-based control of variability and tolerance in off-site manufacture and assembly: optimization of penalty on poor production quality , 2020, Construction Management and Economics.

[43]  I. M. Ramdan,et al.  Association of Demographic Characteristics of Construction Workers and Work Environments to Workplace Accident in High Building (Hotel) Construction , 2019 .

[44]  Hanguk Ryu,et al.  Predicting types of occupational accidents at construction sites in Korea using random forest model , 2019 .

[45]  John Z. Wu,et al.  Assessing work-related risk factors for musculoskeletal knee disorders in construction roofing tasks. , 2019, Applied ergonomics.

[46]  Thomas Renault,et al.  Sentiment analysis and machine learning in finance: a comparison of methods and models on one million messages , 2019, Digital Finance.

[47]  Yasser Elfahham,et al.  Estimation and prediction of construction cost index using neural networks, time series, and regression , 2019, Alexandria Engineering Journal.

[48]  Jhareswar Maiti,et al.  Application of optimized machine learning techniques for prediction of occupational accidents , 2019, Comput. Oper. Res..

[49]  Qingwen Xu,et al.  Collaborative information integration for construction safety monitoring , 2019, Automation in Construction.

[50]  Sohail Asghar,et al.  A Classification Model For Class Imbalance Dataset Using Genetic Programming , 2019, IEEE Access.

[51]  J. Johansson,et al.  Occupational accidents in Swedish construction trades , 2019, International journal of occupational safety and ergonomics : JOSE.

[52]  Francis K. W. Wong,et al.  Interrelation between Human Factor–Related Accidents and Work Patterns in Construction Industry , 2019, Journal of Construction Engineering and Management.

[53]  R. Baker,et al.  Severity of U.S. Construction Worker Injuries, 2015-2017 , 2019, E3S Web of Conferences.

[54]  E. Albrechtsen,et al.  Causal factors and connections in construction accidents , 2019, Safety Science.

[55]  Germán G. Creamer,et al.  Machine Learning in Energy Economics and Finance: A Review , 2018, Energy Economics.

[56]  M. Marzouk,et al.  Assessing Egyptian construction projects performance using principal component analysis , 2018, International Journal of Productivity and Performance Management.

[57]  Theodora Chaspari,et al.  Automated ergonomic risk monitoring using body-mounted sensors and machine learning , 2018, Adv. Eng. Informatics.

[58]  T. Driscoll,et al.  The Epidemiology, Cost, and Occupational Context of Spinal Injuries Sustained While ‘Working for Income’ in NSW: A Record-Linkage Study , 2018, International journal of environmental research and public health.

[59]  Yang Miang Goh,et al.  Safety leading indicators for construction sites: A machine learning approach , 2018, Automation in Construction.

[60]  Eirik Albrechtsen,et al.  Accident types and barrier failures in the construction industry , 2018, Safety Science.

[61]  S. K. Ramaswamy,et al.  Using workers’ compensation claims data to characterize occupational injuries in the biofuels industry , 2018 .

[62]  Fereidoon Moghadas Nejad,et al.  Evaluation of pavement surface drainage using an automated image acquisition and processing system , 2018 .

[63]  AH El-Gilany,et al.  Risk factors for non-fatal occupational injuries among construction workers: A case–control study , 2018, Toxicology and industrial health.

[64]  Tak Wing Yiu,et al.  Does company size matter? Validation of an integrative model of safety behavior across small and large construction companies. , 2017, Journal of safety research.

[65]  Carl T. Haas,et al.  Identifying poses of safe and productive masons using machine learning , 2017 .

[66]  Heng Li,et al.  Biomechanical analysis of risk factors for work-related musculoskeletal disorders during repetitive lifting task in construction workers , 2017 .

[67]  Brenda McCabe,et al.  Impact of individual resilience and safety climate on safety performance and psychological stress of construction workers: A case study of the Ontario construction industry. , 2017, Journal of safety research.

[68]  Dursun Delen,et al.  Investigating injury severity risk factors in automobile crashes with predictive analytics and sensitivity analysis methods , 2017 .

[69]  Matthew R. Hallowell,et al.  Application of machine learning to construction injury prediction , 2016 .

[70]  Q. Zou,et al.  Finding the Best Classification Threshold in Imbalanced Classification , 2016, Big Data Res..

[71]  Evan A. Nadhim,et al.  Falls from Height in the Construction Industry: A Critical Review of the Scientific Literature , 2016, International journal of environmental research and public health.

[72]  Zong Tian,et al.  Investigating driver injury severity patterns in rollover crashes using support vector machine models. , 2016, Accident; analysis and prevention.

[73]  A. Moghimbeigi,et al.  Key factors contributing to accident severity rate in construction industry in Iran: a regression modelling approach / Primjena regresijskog modela u analizi ključnih čimbenika koji pridonose težini nesreća u građevinskoj industriji u Iranu , 2016, Arhiv za higijenu rada i toksikologiju.

[74]  Elaheh Kabir-Mokamelkhah,et al.  Factors associated with the severity of fatal accidents in construction workers , 2016, Medical journal of the Islamic Republic of Iran.

[75]  Nando de Freitas,et al.  Taking the Human Out of the Loop: A Review of Bayesian Optimization , 2016, Proceedings of the IEEE.

[76]  Peter A. Flach,et al.  Precision-Recall-Gain Curves: PR Analysis Done Right , 2015, NIPS.

[77]  Seyed Bagher Mortazavi,et al.  Assessment of accident severity in the construction industry using the Bayesian theorem , 2015, International journal of occupational safety and ergonomics : JOSE.

[78]  Ercan Erdis,et al.  Decision tree analysis of construction fall accidents involving roofers , 2015, Expert Syst. Appl..

[79]  Kasra Madadipouya,et al.  A NEW DECISION TREE METHOD FOR DATA MINING IN MEDICINE , 2015 .

[80]  Darrin Adams,et al.  Twenty years of workers' compensation costs due to falls from height among union carpenters, Washington state. , 2014, American journal of industrial medicine.

[81]  Juan Carlos Rubio-Romero,et al.  Severity of electrical accidents in the construction industry in Spain. , 2014, Journal of safety research.

[82]  C. Ekpenyong,et al.  Associations Between Worker Characteristics, Workplace Factors, and Work-Related Musculoskeletal Disorders: A Cross-Sectional Study of Male Construction Workers in Nigeria , 2014, International journal of occupational safety and ergonomics : JOSE.

[83]  Sherif Mostafa,et al.  Factors associated with the severity of construction accidents: The case of South Australia , 2013 .

[84]  Prasanta Kumar Dey,et al.  Project risk management using multiple criteria decision-making technique and decision tree analysis: a case study of Indian oil refinery , 2012 .

[85]  Sou-Sen Leu,et al.  Applying data mining techniques to explore factors contributing to occupational injuries in Taiwan's construction industry. , 2012, Accident; analysis and prevention.

[86]  Maher Maalouf,et al.  Logistic regression in data analysis: an overview , 2011, Int. J. Data Anal. Tech. Strateg..

[87]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[88]  Sou-Sen Leu,et al.  Characteristic analysis of occupational accidents at small construction enterprises , 2010 .

[89]  Thomas Lengauer,et al.  Permutation importance: a corrected feature importance measure , 2010, Bioinform..

[90]  Hugo Priemus,et al.  Construction safety: An analysis of systems failure: The case of the multifunctional Bos & Lommerplein estate, Amsterdam , 2010 .

[91]  F. Abu-Zidan,et al.  Occupational injury in the United Arab Emirates: epidemiology and prevention. , 2009, Occupational medicine.

[92]  D. Cox.  The Regression Analysis of Binary Sequences , 1958 .