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| 1 | Estimation of flexible pavement structural capacity using machine learning techniques显示文摘The most common index for representing structural condition of the pavement is the structural number.The current procedure for determining structural numbers involves utilizing falling weight deflectometer and ground-penetrating radar tests,recording pavement surface deflections,and analyzing recorded deflections by back-calculation manners.This procedure has two drawbacks:falling weight deflectometer and ground-penetrating radar are expensive tests;back-calculation ways has some inherent shortcomings compared to exact methods as they adopt a trial and error approach.In this study,three machine learning methods entitled Gaussian process regression,M5P model tree,and random forest used for the prediction of structural numbers in flexible pavements.Dataset of this paper is related to 759 flexible pavement sections at Semnan and Khuzestan provinces in Iran and includes“structural number”as output and“surface deflections and surface temperature”as inputs.The accuracy of results was examined based on three criteria of R,MAE,and RMSE.Among the methods employed in this paper,random forest is the most accurate as it yields the best values for above criteria(R=0.841,MAE=0.592,and RMSE=0.760).The proposed method does not require to use ground penetrating radar test,which in turn reduce costs and work difficulty.Using machine learning methods instead of back-calculation improves the calculation process quality and accuracy. | Nader KARBALLAEEZADEH Hosein GHASEMZADEH TEHRANI Danial MOHAMMADZADEH SHADMEHRI Shahaboddin SHAMSHIRBAND | 2020 | Frontiers of Structural and Civil Engineering2020，14，5： | 1 |
| 2 | Predicting lateral displacement caused by seismic liquefaction and performing parametric sensitivity analysis:Considering cumulative absolute velocity and fine content显示文摘Lateral displacement due to liquefaction(D_(H))is the most destructive effect of earthquakes in saturated loose or semi-loose sandy soil.Among all earthquake parameters,the standardized cumulative absolute velocity(CAV_(5))exhibits the largest correlation with increasing pore water pressure and liquefaction.Furthermore,the complex effect of fine content(FC)at different values has been studied and demonstrated.Nevertheless,these two contexts have not been entered into empirical and semi-empirical models to predict D_(H)This study bridges this gap by adding CAV_(5)to the data set and developing two artificial neural network(ANN)models.The first model is based on the entire range of the parameters,whereas the second model is based on the samples with FC values that are less than the 28%critical value.The results demonstrate the higher accuracy of the second model that is developed even with less data.Additionally,according to the uncertainties in the geotechnical and earthquake parameters,sensitivity analysis was performed via Monte Carlo simulation(MCS)using the second developed ANN model that exhibited higher accuracy.The results demonstrated the significant influence of the uncertainties of earthquake parameters on predicting D_(H). | Nima PIRHADI Xiaowei TANG Qing YANG Afshin ASADI Hazem Samih MOHAMED | 2021 | Frontiers of Structural and Civil Engineering2021，15，2： | 1 |
| 3 | A step forward towards a comprehensive framework for assessing liquefaction land damage vulnerability:Exploration from historical data显示文摘The unprecedented liquefaction-related land damage during earthquakes has highlighted the need to develop a model that better interprets the liquefaction land damage vulnerability(LLDV)when determining whether liquefaction is likely to cause damage at the ground's surface.This paper presents the development of a novel comprehensive framework based on select case history records of cone penetration tests using a Bayesian belief network(BBN)methodology to assess seismic soil liquefaction and liquefaction land damage potentials in one model.The BBN-based LLDV model is developed by integrating multi-related factors of seismic soil liquefaction and its induced hazards using a machine learming(ML)algorithm-K2 and domain knowledge(DK)data fusion methodology.Compared with the C4.5 decision tree-J48 model,naive Bayesian(NB)classifier,and BBN-K2 ML prediction methods in terms of overall accuracy and the Cohen's kappa coefficient,the proposed BBN K2 and DK model has a better performance and provides a substitutive novel LLDV framework for characterizing the vulnerability of land to liquefaction-induced damage.The proposed model not only predicts quantitatively the seismic soil liquefaction potential and its ground damage potential probability but can also identify the main reasons and fault-finding state combinations,and the results are likely to assist in decisions on seismic risk mitigation measures for sustainable development.The proposed model is simple to perform in practice and provides a step toward a more sophisticated liquefaction risk assessment modeling.This study also interprets the BBN model sensitivity analysis and most probable explanation of seismic soil liquefed sites based on an engineering point of view. | Mahmood AHMAD Xiao-Wei TANG Jiang-Nan QIU Feezan AHMAD Wen-Jing GU | 2020 | Frontiers of Structural and Civil Engineering2020，14，6： | 0 |
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