A constrained neural network model for soil liquefaction assessment with global applicability

A constrained back propagation neural network(C-BPNN)model for standard penetration test based soil liquefaction assessment with global applicability is developed,incorporating existing knowledge for liquefaction triggering mechanism and empirical relationships.For its development and validation,a comprehensive liquefaction data set is compiled,covering more than 600 liquefaction sites from 36 earthquakes in 10 countries over 50 years with 13 complete information entries.The C-BPNN model design procedure for liquefaction assessment is established by considering appropriate constraints,input data selection,and computation and calibration procedures.Existing empirical relationships for overburden correction and fines content adjustment are shown to be able to improve the prediction success rate of the neural network model,and are thus adopted as constraints for the C-BPNN model.The effectiveness of the C-BPNN method is validated using the liquefaction data set and compared with that of several liquefaction assessment methods currently adopted in engineering practice.The C-BPNN liquefaction model is shown to have improved prediction accuracy and high global adaptability.

Effect of dry density on the liquefaction behaviour of Quaternary silt

Quaternary silt is widely distributed in China and easily liquefies during earthquakes. To identify the influence of the dry density on the liquefaction behaviour of Quaternary silt, 40 cyclic triaxial liquefaction tests were performed on loose silt(dry density rd=1.460 g/cm^3) and dense silt(rd=1.586 g/cm^3) under different cyclic stress ratios(CSRs) to obtain liquefaction assessment criteria, determine the liquefaction resistance, improve the excess pore water pressure(EPWP) growth model and clarify the relationship between the shear modulus and damping ratio. The results indicate that the initial liquefaction assessment criteria for the loose and dense silts are a double-amplitude axial strain of 5% and an EPWP ratio of 1. The increase in the anti-liquefaction ability for the dense silt is more significant under lower confining pressures. The CSR of loose silt falls well within the results of the sandy silt and Fraser River silt, and the dense silt exhibits a higher liquefaction resistance than the sand-silt mixture. The relationships between the CSR and loading cycles were obtained at a failure strain of 1%. The EPWP development in the dense and loose silts complies with the "fast-stable" and "fast-gentle-sharp" growth modes, respectively. The power function model can effectively describe the EPWP growth characteristics of the dense silt. Finally, based on the liquefaction behaviour of silt, a suggestion for reinforcing silt slopes or foundations is proposed.

A Probabilistic Liquefaction Hazard Assessment for Urban Regions Based on Dynamics Analysis Considering Soil Uncertainties

Earthquake induced liquefaction is one of the main geo-disasters threating urban regions, which not only causes direct damages to buildings, but also delays both real-time disaster relief actions and reconstruction activities. It is thus important to assess liquefaction hazard of urban regions effectively and efficiently for disaster prevention and mitigation. Conventional assessment approaches rely on engineering indices such as the factor of safety(FS) against liquefaction, which cannot take into account directly the uncertainties of soils. In contrast, a physics simulation-based approach, by solving soil dynamics problems coupled with excess pore water pressure(EPWP) it is possible to model the uncertainties directly via Monte Carlo simulations. In this study, we demonstrate the capability of such an approach for assessing an urban region with over 10 000 sites. The permeability parameters are assumed to follow a base-10-lognormal distribution among 100 model analyses for each site. A dynamic simulation is conducted for each model analysis to obtain the EPWP results. Based on over 1 million EPWP analysis models, we obtained a probabilistic liquefaction assessment. Empowered by high performance computing, we present for the first time a probabilistic liquefaction hazard assessment for urban regions based on dynamics analysis, which consider soil uncertainties.