[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Kang et al., 2019 - Google Patents

Predicting types of occupational accidents at construction sites in Korea using random forest model

Kang et al., 2019

Document ID
7773215958456679931
Author
Kang K
Ryu H
Publication year
Publication venue
Safety Science

External Links

Snippet

Although industrial accident rates are gradually decreasing in Korea, the construction industry's accident rate is still higher compared with other industries. Human errors, mentally unstable workers, insufficient safety training, and safety policy affect the occurrence of …
Continue reading at www.sciencedirect.com (other versions)

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00Administration; Management
    • G06Q10/06Resources, workflows, human or project management, e.g. organising, planning, scheduling or allocating time, human or machine resources; Enterprise planning; Organisational models
    • G06Q10/063Operations research or analysis
    • G06Q10/0635Risk analysis
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00Administration; Management
    • G06Q10/06Resources, workflows, human or project management, e.g. organising, planning, scheduling or allocating time, human or machine resources; Enterprise planning; Organisational models
    • G06Q10/063Operations research or analysis
    • G06Q10/0639Performance analysis
    • G06Q10/06393Score-carding, benchmarking or key performance indicator [KPI] analysis
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00Administration; Management
    • G06Q10/04Forecasting or optimisation, e.g. linear programming, "travelling salesman problem" or "cutting stock problem"
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00Computer systems utilising knowledge based models
    • G06N5/02Knowledge representation
    • G06N5/022Knowledge engineering, knowledge acquisition
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q40/00Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G06Q40/08Insurance, e.g. risk analysis or pensions
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00Subject matter not provided for in other groups of this subclass
    • G06N99/005Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00Computer systems utilising knowledge based models
    • G06N5/04Inference methods or devices
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/02Computer systems based on biological models using neural network models
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N7/00Computer systems based on specific mathematical models
    • G06N7/005Probabilistic networks
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q40/00Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G06Q40/02Banking, e.g. interest calculation, credit approval, mortgages, home banking or on-line banking
    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B23/00Testing or monitoring of control systems or parts thereof
    • G05B23/02Electric testing or monitoring
    • G05B23/0205Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults

Similar Documents

Publication Publication Date Title
Kang et al. Predicting types of occupational accidents at construction sites in Korea using random forest model
Sarkar et al. Predicting and analyzing injury severity: A machine learning-based approach using class-imbalanced proactive and reactive data
Pan et al. Modeling risks in dependent systems: A Copula-Bayesian approach
Mistikoglu et al. Decision tree analysis of construction fall accidents involving roofers
Tsoukalas et al. Prediction of occupational risk in the shipbuilding industry using multivariable linear regression and genetic algorithm analysis
Ayhan et al. Safety assessment in megaprojects using artificial intelligence
Jahangiri et al. A neuro-fuzzy risk prediction methodology for falling from scaffold
Wang et al. An application of nonlinear fuzzy analytic hierarchy process in safety evaluation of coal mine
KR101652099B1 (en) Risk map based on gas accident response and prevention system
Koc et al. Prediction of construction accident outcomes based on an imbalanced dataset through integrated resampling techniques and machine learning methods
Fragiadakis et al. An adaptive neuro-fuzzy inference system (anfis) model for assessing occupational risk in the shipbuilding industry
Wang et al. Probabilistic risk assessment of tunneling-induced damage to existing properties
CN113379267B (en) Urban fire event processing method, system and storage medium based on risk classification prediction
Şen Supervised fuzzy logic modeling for building earthquake hazard assessment
Nallathambi et al. Prediction of influencing atmospheric conditions for explosion Avoidance in fireworks manufacturing Industry-A network approach
Kung et al. Designing intelligent disaster prediction models and systems for debris-flow disasters in Taiwan
Can et al. A novel fuzzy risk matrix based risk assessment approach
Sarkar et al. An integrated approach using rough set theory, ANFIS, and Z-number in occupational risk prediction
Hayati et al. Risk assessment using fuzzy FMEA (case study: Tehran subway tunneling operations)
Gondia et al. Machine learning–based decision support framework for construction injury severity prediction and risk mitigation
Azadeh et al. An adaptive algorithm for assessment of operators with job security and HSEE indicators
Lu et al. Using cased based reasoning for automated safety risk management in construction industry
Ma et al. Development of a time-variant causal model of human error in construction with dynamic Bayesian network
Soghrati Ghasbeh et al. Equitable post-disaster relief distribution: a robust multi-objective multi-stage optimization approach
Beeche et al. Computational risk modeling of underground coal mines based on NIOSH employment demographics