Ultimate bearing capacity(UBC)is a key subject in geotechnical/foundation engineering as it determines the limit of loads imposed on the foundation.The most reliable means of determining UBC is through experiment,but ...Ultimate bearing capacity(UBC)is a key subject in geotechnical/foundation engineering as it determines the limit of loads imposed on the foundation.The most reliable means of determining UBC is through experiment,but it is costly and time-consuming which has led to the development of various models based on the simplified assumptions.The outcomes of the models are usually validated with the experimental results,but a large gap usually exists between them.Therefore,a model that can give a close prediction of the experimental results is imperative.This study proposes a grasshopper optimization algorithm(GOA)and salp swarm algorithm(SSA)to optimize artificial neural networks(ANNs)using the existing UBC experimental database.The performances of the proposed models are evaluated using various statistical indices.The obtained results are compared with the existing models.The proposed models outperformed the existing models.The proposed hybrid GOA-ANN and SSA-ANN models are then transformed into mathematical forms that can be incorporated into geotechnical/foundation engineering design codes for accurate UBC measurements.展开更多
Rock masses are commonly used as the underlying layer of important structures such as bridges, dams and transportation constructions. The success of a foundation design for such structures mainly depends on the accura...Rock masses are commonly used as the underlying layer of important structures such as bridges, dams and transportation constructions. The success of a foundation design for such structures mainly depends on the accuracy of estimating the bearing capacity of rock beneath them. Several traditional numerical approaches are proposed for the estimation of the bearing capacity of foundations resting on rock masses to avoid performing elaborate and expensive experimental studies. Despite this fact, there still exists a serious need to develop more robust predictive models. This paper proposes new nonlinear prediction models for the ultimate bearing capacity of shallow foundations resting on non-fractured rock masses using a novel evolutionary computational approach, called linear genetic programming. A comprehensive set of rock socket, centrifuge rock socket, plate load and large-scaled footing load test results is used to develop the models. In order to verify the validity of the models, the sensitivity analysis is conducted and discussed. The results indicate that the proposed models accurately characterize the bearing capacity of shallow foundations. The correlation coefficients between the experimental and predicted bearing capacity values are equal to 0.95 and 0.96 for the best LGP models. Moreover, the derived models reach a notably better prediction performance than the traditional equations.展开更多
将并行计算理论和方法引入到导管架碰撞极限承载力的非线性分析中,根据美国石油学会API RP 2A-WSD标准中的方法建立桩腿非线性抗侧力-位移曲线,考虑桩-土非线性的特点分析导管架碰撞极限承载力。利用该方法对埕岛油田某导管架平台的碰...将并行计算理论和方法引入到导管架碰撞极限承载力的非线性分析中,根据美国石油学会API RP 2A-WSD标准中的方法建立桩腿非线性抗侧力-位移曲线,考虑桩-土非线性的特点分析导管架碰撞极限承载力。利用该方法对埕岛油田某导管架平台的碰撞极限承载力进行研究,分别得到平台在碰撞力作用下的平台顶部荷载-位移曲线、主桩腿弯矩变化、Mises应力变化曲线等。将集群并行运算的结果与单一PC机的结果进行对比,验证并行计算的计算精度和计算效率,同时研究不同影响因素对并行加速比和并行效率的影响。计算结果表明:平台主桩腿最大位移、应力随碰撞位置的降低而增大;碰撞位置越低,平台的碰撞极限承载能力越大;并行计算所得到的结果与单一PC机运算得到的结果相差很小,是可信的;并行加速比随着参与并行结点数的增加而增大,并行效率随着参与并行结点数的增加而下降;随着模型节点和单元数目的增多,集群的并行效率提高,并且越复杂的模型和结构在进行计算时集群并行计算能力的优势越明显。展开更多
基金supported by Korea Research Fellowship Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Science and ICT(Grant No.2019H1D3A1A01102993)the Inha University Research Grant(2022).
文摘Ultimate bearing capacity(UBC)is a key subject in geotechnical/foundation engineering as it determines the limit of loads imposed on the foundation.The most reliable means of determining UBC is through experiment,but it is costly and time-consuming which has led to the development of various models based on the simplified assumptions.The outcomes of the models are usually validated with the experimental results,but a large gap usually exists between them.Therefore,a model that can give a close prediction of the experimental results is imperative.This study proposes a grasshopper optimization algorithm(GOA)and salp swarm algorithm(SSA)to optimize artificial neural networks(ANNs)using the existing UBC experimental database.The performances of the proposed models are evaluated using various statistical indices.The obtained results are compared with the existing models.The proposed models outperformed the existing models.The proposed hybrid GOA-ANN and SSA-ANN models are then transformed into mathematical forms that can be incorporated into geotechnical/foundation engineering design codes for accurate UBC measurements.
文摘Rock masses are commonly used as the underlying layer of important structures such as bridges, dams and transportation constructions. The success of a foundation design for such structures mainly depends on the accuracy of estimating the bearing capacity of rock beneath them. Several traditional numerical approaches are proposed for the estimation of the bearing capacity of foundations resting on rock masses to avoid performing elaborate and expensive experimental studies. Despite this fact, there still exists a serious need to develop more robust predictive models. This paper proposes new nonlinear prediction models for the ultimate bearing capacity of shallow foundations resting on non-fractured rock masses using a novel evolutionary computational approach, called linear genetic programming. A comprehensive set of rock socket, centrifuge rock socket, plate load and large-scaled footing load test results is used to develop the models. In order to verify the validity of the models, the sensitivity analysis is conducted and discussed. The results indicate that the proposed models accurately characterize the bearing capacity of shallow foundations. The correlation coefficients between the experimental and predicted bearing capacity values are equal to 0.95 and 0.96 for the best LGP models. Moreover, the derived models reach a notably better prediction performance than the traditional equations.
文摘将并行计算理论和方法引入到导管架碰撞极限承载力的非线性分析中,根据美国石油学会API RP 2A-WSD标准中的方法建立桩腿非线性抗侧力-位移曲线,考虑桩-土非线性的特点分析导管架碰撞极限承载力。利用该方法对埕岛油田某导管架平台的碰撞极限承载力进行研究,分别得到平台在碰撞力作用下的平台顶部荷载-位移曲线、主桩腿弯矩变化、Mises应力变化曲线等。将集群并行运算的结果与单一PC机的结果进行对比,验证并行计算的计算精度和计算效率,同时研究不同影响因素对并行加速比和并行效率的影响。计算结果表明:平台主桩腿最大位移、应力随碰撞位置的降低而增大;碰撞位置越低,平台的碰撞极限承载能力越大;并行计算所得到的结果与单一PC机运算得到的结果相差很小,是可信的;并行加速比随着参与并行结点数的增加而增大,并行效率随着参与并行结点数的增加而下降;随着模型节点和单元数目的增多,集群的并行效率提高,并且越复杂的模型和结构在进行计算时集群并行计算能力的优势越明显。
