Landslides are natural phenomena, causing serious fatalities and negative impacts on socioeconomic. The Three Gorges Reservoir (TGR) area of China is characterized by more prone to landslides for the rainfall and variation of reservoir level. Prediction of landslide displacement is favorable for the establishment of early geohazard warning system. Conventional machine learning methods as forecasting models often suffer gradient disappearance and explosion, or training is slow. Hence, a dynamic method for displacement prediction of the step-wise landslide is provided, which is based on gated recurrent unit (GRU) model with time series analysis. The establishment process of this method is interpreted and applied to Erdaohe landslide induced by multi-factors in TGR area: the accumulative displacements of landslide are obtained by the global positioning system; the measured accumulative displacements is decomposed into the trend and periodic displacements by moving average method; the predictive trend displacement is fitted by a cubic polynomial; and the periodic displacement is obtained by the GRU model training. And the support vector machine (SVM) model and GRU model are used as comparisons. It is verified that the proposed method can quite accurately predict the displacement of the landslide, which benefits for effective early geological hazards warning system. Moreover, the proposed method has higher prediction accuracy than the SVM model.

A novel displacement prediction method using gated recurrent unit model with time series analysis in the Erdaohe landslide

In order to study the influence of water level fluctuation on the stability of landslide in the Three Gorges Reservoir (TGR), Xigouwan landslide was selected to be a research object to analyze its deformation mechanism as the fluctuation of the reservoir water level. Based on the long-term field monitoring of landslide, the evolution law of the surface has been studied. Based on the numerical simulation used by FLAC3D, five different water levels of TGR operating conditions are set, and the plastic zone, the factor of safety, change of displacement, and the seepage characteristics of the landslide were obtained to analyze the influence of water level fluctuation on the stability of landslide.

The influence of water level fluctuation on the stability of landslide in the Three Gorges Reservoir

Prediction of residual behaviour for post-earthquake damaged reinforced concrete column based on damage distribution model

With China’s government facilitating railway projects, more railway lines inevitably pass through ecological fragile regions (EFRs). Railway construction activities in EFRs might cause detrimental impacts on the local water-soil environment (WSE), which is the basis of the local ecological system that if destroyed can induce secondary disasters. Studies on the WSE risk (WSER) during railway construction in EFRs are limited. As such, this study aims to offer preliminary insight into the WSER assessment of railway construction in EFRs. WSERs were identified firstly based on the literature review and field surveys, and thus a risk index framework for WSER assessment including 5 categories of WSERs and 16 second-order risks was established. Then a comprehensive quantitative assessment method was developed by integrating analytic hierarchy process (AHP) and matter-element analysis (MEA) to assess the overall WSERs of railway construction in EFRs. A case (i.e., the Mingan subproject of Hefei-Fuzhou railway) was selected to demonstrate and validate the developed approach. Results show that the proposed assessment approach can be applied to evaluate the WSERs during railway construction. In addition, the case study demonstrates that the risk of construction methods should be the key focus. Findings from this study enrich the knowledge body of sustainable railways and guide the project managers to conduct practical WSER assessment of railway construction.

https://www.mdpi.com/2071-1050/12/19/7910/pdf

A Comprehensive Assessment Approach for Water-Soil Environmental Risk during Railway Construction in Ecological Fragile Region Based on AHP and MEA

Seismic behavior of frost-damaged squat RC shear walls under artificial climate environment: a further experimental research

Bond-slip model considering freeze-thaw damage effect of concrete and its application

Machine learning-based collapse prediction for post-earthquake damaged RC columns under subsequent earthquakes

Quantitative damage evaluation of prestressed concrete containments with steel fiber strengthening under internal pressure

Seismic damage to components and structures may cause extensive degradation of the seismic capacities of the damaged buildings. The analysis of the residual hysteretic behavior of post-earthquake damaged components and structures is a critical step in the assessment of their residual seismic capacities. Whereas, the difficulty in quantifying the damage distribution of materials in post-earthquake damaged components leads to the challenge of quantitative analysis of their residual behavior (i.e. residual stiffness, strength, energy dissipation). In this study, a visual data-informed fiber beam-column model was developed to analyze the residual hysteretic behavior of post-earthquake damaged RC columns under cyclic loads. To begin with, the method for quantifying the damage strain fields of the columns according to their inspected visual data was proposed. Then, the stress-strain relations of the materials considering the effect of damage strain fields were constructed, and the visual data-informed fiber beam-column model was proposed for the analysis of the residual hysteretic behavior of post-earthquake damaged RC columns subjected to cyclic lateral loads. At last, the proposed model was validated by comparing model results with existing test data. The comparison shows that the proposed visual data-informed fiber beam-column model can accurately analyze the concrete crushing surfaces, the residual load-displacement responses, and the energy dissipation capacities of post-earthquake damaged RC columns, facilitating the evaluation of the residual seismic capacities of post-earthquake damaged RC columns. 

Wen-Tao Wang

Papers

Bond-slip model considering freeze-thaw damage effect of concrete and its application

Machine learning-based collapse prediction for post-earthquake damaged RC columns under subsequent earthquakes

Quantitative damage evaluation of prestressed concrete containments with steel fiber strengthening under internal pressure

A Visual Data-Informed Fiber Beam-Column Model for the Analysis of Residual Hysteretic Behavior of Post-Earthquake Damaged RC Columns