Matches in SemOpenAlex for { <https://semopenalex.org/work/W3117942735> ?p ?o ?g. }
- W3117942735 endingPage "103490" @default.
- W3117942735 startingPage "103490" @default.
- W3117942735 abstract "This paper presents a brief review on major accidents and conducts bibliometric analysis of risk assessment methods for excavation system in recent year. The summarization of potential risks during excavation provides an important index for establishing an early warning system. The applications of fuzzy set theory and machine learning methods in risk assessment during excavation are presented. A case study of excavation in Guangzhou metro station is used to demonstrate the application of a machine learning method for risk evaluation. The large amount of data collected by 3S techniques (RS, GIS and GPS) and sensors increases accuracy of risk assessment levels in excavation. These procedures, integrated into building information modelling (BIM) management platform, can manipulate dynamic safety risk monitoring, control, and management. Finally, the processing and analysis of big data obtained from 3S techniques and sensors provide promising perspectives for establishing integrated technology system for excavation. • Potential risks occurred during excavation construction are summarized. • The methods for risk assessment of excavation are presented. • Characteristics of excavation risk assessment: (i) subjective to objective; (2) qualification to quantification. • Random forest model for evaluating risk for excavation system is illustrated. • Perspective using advanced technologies for excavation management is proposed." @default.
- W3117942735 created "2021-01-05" @default.
- W3117942735 creator A5046193097 @default.
- W3117942735 creator A5050583971 @default.
- W3117942735 creator A5054209131 @default.
- W3117942735 creator A5056380462 @default.
- W3117942735 date "2021-02-01" @default.
- W3117942735 modified "2023-09-29" @default.
- W3117942735 title "Risk assessment and management of excavation system based on fuzzy set theory and machine learning methods" @default.
- W3117942735 cites W1498436455 @default.
- W3117942735 cites W1668569279 @default.
- W3117942735 cites W1968275355 @default.
- W3117942735 cites W1975481270 @default.
- W3117942735 cites W1978894336 @default.
- W3117942735 cites W1980452149 @default.
- W3117942735 cites W1980564456 @default.
- W3117942735 cites W1980841949 @default.
- W3117942735 cites W1980973394 @default.
- W3117942735 cites W1983210776 @default.
- W3117942735 cites W2011702380 @default.
- W3117942735 cites W2013932252 @default.
- W3117942735 cites W2016179164 @default.
- W3117942735 cites W2026410682 @default.
- W3117942735 cites W2031703450 @default.
- W3117942735 cites W2042102899 @default.
- W3117942735 cites W2067627945 @default.
- W3117942735 cites W2081849111 @default.
- W3117942735 cites W2094259335 @default.
- W3117942735 cites W2122145224 @default.
- W3117942735 cites W2123754130 @default.
- W3117942735 cites W2125068387 @default.
- W3117942735 cites W2130016085 @default.
- W3117942735 cites W2147228094 @default.
- W3117942735 cites W2150220236 @default.
- W3117942735 cites W2178939760 @default.
- W3117942735 cites W2477128123 @default.
- W3117942735 cites W2512479836 @default.
- W3117942735 cites W2581855983 @default.
- W3117942735 cites W2582146055 @default.
- W3117942735 cites W2731637685 @default.
- W3117942735 cites W2739589480 @default.
- W3117942735 cites W2754789423 @default.
- W3117942735 cites W2755885017 @default.
- W3117942735 cites W2761171919 @default.
- W3117942735 cites W2768163011 @default.
- W3117942735 cites W2789555074 @default.
- W3117942735 cites W2790304257 @default.
- W3117942735 cites W2794572842 @default.
- W3117942735 cites W2796224854 @default.
- W3117942735 cites W2804432451 @default.
- W3117942735 cites W2884762388 @default.
- W3117942735 cites W2892755442 @default.
- W3117942735 cites W2896286460 @default.
- W3117942735 cites W2900292137 @default.
- W3117942735 cites W2905944349 @default.
- W3117942735 cites W2911964244 @default.
- W3117942735 cites W2921922207 @default.
- W3117942735 cites W2951787506 @default.
- W3117942735 cites W2954289684 @default.
- W3117942735 cites W2954621195 @default.
- W3117942735 cites W2954869684 @default.
- W3117942735 cites W2959787411 @default.
- W3117942735 cites W2963929932 @default.
- W3117942735 cites W2965614847 @default.
- W3117942735 cites W2970263100 @default.
- W3117942735 cites W2991653145 @default.
- W3117942735 cites W2993667365 @default.
- W3117942735 cites W2996045208 @default.
- W3117942735 cites W3000372520 @default.
- W3117942735 cites W3004765871 @default.
- W3117942735 cites W3006637824 @default.
- W3117942735 cites W3007454630 @default.
- W3117942735 cites W3008829399 @default.
- W3117942735 cites W3014784156 @default.
- W3117942735 cites W3014913272 @default.
- W3117942735 cites W3081821516 @default.
- W3117942735 cites W3087472426 @default.
- W3117942735 cites W4239510810 @default.
- W3117942735 doi "https://doi.org/10.1016/j.autcon.2020.103490" @default.
- W3117942735 hasPublicationYear "2021" @default.
- W3117942735 type Work @default.
- W3117942735 sameAs 3117942735 @default.
- W3117942735 citedByCount "93" @default.
- W3117942735 countsByYear W31179427352021 @default.
- W3117942735 countsByYear W31179427352022 @default.
- W3117942735 countsByYear W31179427352023 @default.
- W3117942735 crossrefType "journal-article" @default.
- W3117942735 hasAuthorship W3117942735A5046193097 @default.
- W3117942735 hasAuthorship W3117942735A5050583971 @default.
- W3117942735 hasAuthorship W3117942735A5054209131 @default.
- W3117942735 hasAuthorship W3117942735A5056380462 @default.
- W3117942735 hasConcept C107053488 @default.
- W3117942735 hasConcept C112930515 @default.
- W3117942735 hasConcept C12174686 @default.
- W3117942735 hasConcept C127413603 @default.
- W3117942735 hasConcept C146978453 @default.
- W3117942735 hasConcept C154945302 @default.
- W3117942735 hasConcept C162324750 @default.