Assessment of seismic amplification factor of excavation with support system     

Tavakoli  Hamidreza  Kutanaei  Saman Soleimani  Hosseini  Seyed Hossein 《地震工程与工程振动(英文版)》2019,18(3):555-566

Retaining walls have been used in many construction projects such as for road and inclined surfaces protection. The damage caused by an earthquake depends on the fundamental frequency, amplitude and the duration of the seismic motion. These parameters strongly depend on the seismic properties of the layers that are near the surface. In the study of retaining walls, in addition to the influence of soil, the influence of topography is also important. In the present study, site response analysis is performed by using finite element software PLAXIS to obtain the effect of various factors such as embedded length of the sheet pile, underground water table, length and angle of the nail, shear wave velocity of soil on site effect and dynamic response. Moreover, for better understanding of the effect of the above parameters, the stability analysis was performed by using shear reduction method. The results show that an increase in the embedded length of the sheet pile and the length of nailing causes an increase in the amplification factor. Moreover, for shear-wave velocity in the range of 200-600 m/s, the amplification factor increases with increase of the shear-wave velocity due to the decrease of nonlinear behavior.  相似文献   

Improving the coastal aquifers’ vulnerability assessment using SCMAI ensemble of three machine learning approaches     

Bordbar  Mojgan  Neshat  Aminreza  Javadi  Saman  Pradhan  Biswajeet  Dixon  Barnali  Paryani  Sina 《Natural Hazards》2022,110(3):1799-1820

The main objective of this study is to integrate adaptive neuro-fuzzy inference system (ANFIS), support vector machine (SVM) and artificial neural network (ANN) to design an integrated supervised committee machine artificial intelligence (SCMAI) model to spatially predict the groundwater vulnerability to seawater intrusion in Gharesoo-Gorgan Rood coastal aquifer placed in the northern part of Iran. Six hydrological GALDIT parameters (i.e., G groundwater occurrence, A aquifer hydraulic conductivity, L level of groundwater above sea level, D distance from the shore, I impact of the existing status of seawater intrusion in the region, and T thickness of the aquifer) were considered as inputs for each model. In the training step, the values of GALDIT’s vulnerability index were conditioned by using the values of TDS concentration in order to obtain the conditioned vulnerability index (CVI). The CVI was considered as the target for each model. After training the models, each model was tested using a separate TDS dataset. The results indicated that the ANN and ANFIS algorithms performed better than the SVM algorithm. The values of correlation were obtained as 88, 87, and 80% for ANN, ANFIS, and SVM models, respectively. In the testing step of the SCMAI model, the values of RMSE, R², and r were obtained as 6.4, 0.95, and 97%, respectively. Overall, SCMAI model outperformed other models to spatially predicting vulnerable zones. The result of the SCMAI model confirmed that the western zones along the shoreline had the highest vulnerability to seawater intrusion; therefore, it seems critical to consider emergency protection plans for study area.

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