A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Boreholes plans optimization methodology combining geostatistical simulation and simulated annealing
Nowadays, the prospection plans have the difficult task of ensuring a more complete and rich characterization of the rock mass for the purpose of optimizing costs and increasing safety in geotechnical projects. Currently, boreholes location and depth are mainly defined based on experience and know-how of professionals, as such, it is user-dependent. Hence, there is a lack of methodologies to help the decision-makers in defining the optimal location of boreholes (with relevant information). Therefore, this paper presents a methodology based on the use of geostatistical conditional simulation allied to a stochastic global optimization algorithm (Simulated Annealing) to develop optimized boreholes plans comparing a uni-objective and a multi-criteria optimization approaches. In this work, the optimized location is considered the one that minimizes uncertainty translated by either the average local variance or the average width of 95% probability intervals of simulated values at unsampled locations. This methodology was applied using preliminary information obtained from previously executed boreholes using as variable the empirical rock mass classification system, Rock Mass Rating, in a Chilean deposit. ; This research is inserted in LNEC project named P2I-RockGeoStat and was partially funded by FCT (Fundação para a Ciência e Tecnologia, Portugal) in the scope of project PEst-UID/CEC/00319/2013, included in ISISE project UID/ECl/04029/2013 as well as the PhD grant SFRH/BD/89627/2012, and by the Chilean Commission for Scientific and Technological Research, through Project CONICYT PIA Anillo ACT1407. ; info:eu-repo/semantics/publishedVersion
Boreholes plans optimization methodology combining geostatistical simulation and simulated annealing
Nowadays, the prospection plans have the difficult task of ensuring a more complete and rich characterization of the rock mass for the purpose of optimizing costs and increasing safety in geotechnical projects. Currently, boreholes location and depth are mainly defined based on experience and know-how of professionals, as such, it is user-dependent. Hence, there is a lack of methodologies to help the decision-makers in defining the optimal location of boreholes (with relevant information). Therefore, this paper presents a methodology based on the use of geostatistical conditional simulation allied to a stochastic global optimization algorithm (Simulated Annealing) to develop optimized boreholes plans comparing a uni-objective and a multi-criteria optimization approaches. In this work, the optimized location is considered the one that minimizes uncertainty translated by either the average local variance or the average width of 95% probability intervals of simulated values at unsampled locations. This methodology was applied using preliminary information obtained from previously executed boreholes using as variable the empirical rock mass classification system, Rock Mass Rating, in a Chilean deposit. ; This research is inserted in LNEC project named P2I-RockGeoStat and was partially funded by FCT (Fundação para a Ciência e Tecnologia, Portugal) in the scope of project PEst-UID/CEC/00319/2013, included in ISISE project UID/ECl/04029/2013 as well as the PhD grant SFRH/BD/89627/2012, and by the Chilean Commission for Scientific and Technological Research, through Project CONICYT PIA Anillo ACT1407. ; info:eu-repo/semantics/publishedVersion
Boreholes plans optimization methodology combining geostatistical simulation and simulated annealing
Pinheiro, Marisa Mota (author) / Emery, Xavier (author) / Rocha, Ana Maria A. C. (author) / Miranda, Tiago F. S. (author) / Lamas, Luís (author)
2017-11-01
doi:10.1016/j.tust.2017.07.003
Article (Journal)
Electronic Resource
English
DDC:
690
Boreholes plans optimization methodology combining geostatistical simulation and simulated annealing
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