Recent research has clarified the sequence of ground deformation mechanisms that manifest themselves when excavations are made in soft ground. Furthermore, a new framework to describe the deformability of clays in the...Recent research has clarified the sequence of ground deformation mechanisms that manifest themselves when excavations are made in soft ground. Furthermore, a new framework to describe the deformability of clays in the working stress range has been devised using a large database of previously published soil tests. This paper aims to capitalize on these advances, by analyzing an expanded database of ground movements associated with braced excavations in Shanghai. It is shown that conventional design charts fail to take account either of the characteristics of soil deformability or the relevant deformation mechanisms, and therefore introduce significant scatter. A new method of presentation is found which provides a set of design charts that clarify the influence of soil deformability, wall stiffness, and the geometry of the excavation in relation to the depth of soft ground.展开更多
Effective control of blasting outcomes depends on a thorough understanding of rock geology and the integration of geological characteristics with blast design parameters.This study underscores the importance of adapti...Effective control of blasting outcomes depends on a thorough understanding of rock geology and the integration of geological characteristics with blast design parameters.This study underscores the importance of adapting blast design parameters to geological conditions to optimize the utilization of explosive energy for rock fragmentation.To achieve this,data on fifty geo-blast design parameters were collected and used to train machine learning algorithms.The objective was to develop predictive models for estimating the blast oversize percentage,incorporating seven controlled components and one uncontrollable index.The study employed a combination of hybrid long-short-term memory(LSTM),support vector regression,and random forest algorithms.Among these,the LSTM model enhanced with the tree seed algorithm(LSTM-TSA)demonstrated the highest prediction accuracy when handling large datasets.The LSTM-TSA soft computing model was specifically leveraged to optimize various blast parameters such as burden,spacing,stemming length,drill hole length,charge length,powder factor,and joint set number.The estimated percentage oversize values for these parameters were determined as 0.7 m,0.9 m,0.65 m,1.4 m,0.7 m,1.03 kg/m^(3),35%,and 2,respectively.Application of the LSTM-TSA model resulted in a significant 28.1%increase in the crusher's production rate,showcasing its effectiveness in improving blasting operations.展开更多
文摘Recent research has clarified the sequence of ground deformation mechanisms that manifest themselves when excavations are made in soft ground. Furthermore, a new framework to describe the deformability of clays in the working stress range has been devised using a large database of previously published soil tests. This paper aims to capitalize on these advances, by analyzing an expanded database of ground movements associated with braced excavations in Shanghai. It is shown that conventional design charts fail to take account either of the characteristics of soil deformability or the relevant deformation mechanisms, and therefore introduce significant scatter. A new method of presentation is found which provides a set of design charts that clarify the influence of soil deformability, wall stiffness, and the geometry of the excavation in relation to the depth of soft ground.
基金funded by China Scholarship Council (No.202006370006).
文摘Effective control of blasting outcomes depends on a thorough understanding of rock geology and the integration of geological characteristics with blast design parameters.This study underscores the importance of adapting blast design parameters to geological conditions to optimize the utilization of explosive energy for rock fragmentation.To achieve this,data on fifty geo-blast design parameters were collected and used to train machine learning algorithms.The objective was to develop predictive models for estimating the blast oversize percentage,incorporating seven controlled components and one uncontrollable index.The study employed a combination of hybrid long-short-term memory(LSTM),support vector regression,and random forest algorithms.Among these,the LSTM model enhanced with the tree seed algorithm(LSTM-TSA)demonstrated the highest prediction accuracy when handling large datasets.The LSTM-TSA soft computing model was specifically leveraged to optimize various blast parameters such as burden,spacing,stemming length,drill hole length,charge length,powder factor,and joint set number.The estimated percentage oversize values for these parameters were determined as 0.7 m,0.9 m,0.65 m,1.4 m,0.7 m,1.03 kg/m^(3),35%,and 2,respectively.Application of the LSTM-TSA model resulted in a significant 28.1%increase in the crusher's production rate,showcasing its effectiveness in improving blasting operations.
