Large earthquakes initiate chains of surface processes that last much longer than the brief moments of strong shaking. Most moderate‐ and large‐magnitude earthquakes trigger landslides, ranging from small failures in the soil cover to massive, devastating rock avalanches. Some landslides dam rivers and impound lakes, which can collapse days to centuries later, and flood mountain valleys for hundreds of kilometers downstream. Landslide deposits on slopes can remobilize during heavy rainfall and evolve into debris flows. Cracks and fractures can form and widen on mountain crests and flanks, promoting increased frequency of landslides that lasts for decades. More gradual impacts involve the flushing of excess debris downstream by rivers, which can generate bank erosion and floodplain accretion as well as channel avulsions that affect flooding frequency, settlements, ecosystems, and infrastructure. Ultimately, earthquake sequences and their geomorphic consequences alter mountain landscapes over both human and geologic time scales. Two recent events have attracted intense research into earthquake‐induced landslides and their consequences: the magnitude M 7.6 Chi‐Chi, Taiwan earthquake of 1999, and the M 7.9 Wenchuan, China earthquake of 2008. Using data and insights from these and several other earthquakes, we analyze how such events initiate processes that change mountain landscapes, highlight research gaps, and suggest pathways toward a more complete understanding of the seismic effects on the Earth's surface.

Earthquake-Induced Chains of Geologic Hazards: Patterns, Mechanisms, and Impacts

Artificial neural network (ANN) aimed to simulate the behavior of the nervous system as well as the human brain. Neural network models are mathematical computing systems inspired by the biological neural network in which try to constitute animal brains. ANNs recently extended, presented, and applied by many research scholars in the area of geotechnical engineering. After a comprehensive review of the published studies, there is a shortage of classification of study and research regarding systematic literature review about these approaches. A review of the literature reveals that artificial neural networks is well established in modeling retaining walls deflection, excavation, soil behavior, earth retaining structures, site characterization, pile bearing capacity (both skin friction and end-bearing) prediction, settlement of structures, liquefaction assessment, slope stability, landslide susceptibility mapping, and classification of soils. Therefore, the present study aimed to provide a systematic review of methodologies and applications with recent ANN developments in the subject of geotechnical engineering. Regarding this, a major database of the web of science has been selected. Furthermore, meta-analysis and systematic method which called PRISMA has been used. In this regard, the selected papers were classified according to the technique and method used, the year of publication, the authors, journals and conference names, research objectives, results and findings, and lastly solution and modeling. The outcome of the presented review will contribute to the knowledge of civil and/or geotechnical designers/practitioners in managing information in order to solve most types of geotechnical engineering problems. The methods discussed here help the geotechnical practitioner to be familiar with the limitations and strengths of ANN compared with alternative conventional mathematical modeling methods.

A systematic review and meta-analysis of artificial neural network application in geotechnical engineering: theory and applications

We present an updated geospatial approach to estimation of earthquake‐induced liquefaction from globally available geospatial proxies. Our previous iteration of the geospatial liquefaction model was based on mapped liquefaction surface effects from four earthquakes in Christchurch, New Zealand, and Kobe, Japan, paired with geospatial explanatory variables including slope‐derived V S 30 , compound topographic index, and magnitude‐adjusted peak ground acceleration (PGA) from ShakeMap. The updated geospatial liquefaction model presented herein improves the performance and the generality of the model. The updates include (1) expanding the liquefaction database to 27 earthquake events across six countries, (2) addressing the sampling of nonliquefaction for incomplete liquefaction inventories, (3) testing interaction effects between explanatory variables, and (4) overall improving the model’s performance. We inspected 14 geospatial proxies for soil density and soil saturation; the most promising of these are slope‐derived V S 30 , modeled water table depth, distance to coast, distance to river, distance to the closest water body, and precipitation. We found that peak ground velocity (PGV) performs better than PGA as the shaking intensity parameter. We present two models which offer improved performance over prior models. We evaluate model performance using the area under the receiver operating characteristic curve, and the Brier score. The best‐performing model in a coastal setting uses distance to the coast but is problematic for regions away from the coast. The second best model, using PGV, V S 30 , water table depth, distance to the closest water body, and precipitation, performs better in noncoastal regions and thus is the model we recommend for global implementation.

An Updated Geospatial Liquefaction Model for Global Application

The story of statistics in geotechnical engineering can be traced to Lumb’s classical Canadian Geotechnical Journal paper on “The Variability of Natural Soils” published in 1966. In parallel, the s...

The story of statistics in geotechnical engineering

The prediction of the potential of soil liquefaction induced by the earthquake is a vital task in construction engineering and geotechnical engineering. To provide a possible solution to such problems, this paper proposes two support vector machine (SVM) models which are optimized by genetic algorithm (GA) and grey wolf optimizer (GWO) to predict the potential of soil liquefaction. Field observation data based on cone penetration test (CPT), standard penetration test (SPT) and shear wave velocity (VS) test (SWVT) are employed to verify the reliability of the GA–SVM model and the GWO–SVM model, the numbers of input variables of these three field testing data sets are 6, 12 and 8, respectively, and the output result is the potential of soil liquefaction. To verify whether the two optimization algorithms GA and GWO have significantly improved the performance of SVM model, an unoptimized SVM model is served as a reference in this study. And five performance metrics, including classification accuracy rate (ACC), precision rate (PRE), recall rate (REC), F1 score (F1) and AUC are used to evaluate the classification performance of the three models. Results of the study confirm that when CPT-based, SPT-based and SWVT-based test sets are input into three classification models, the highest classification accuracy of 0.9825, 0.9032 and 0.9231, respectively, is achieved with GWO–SVM. And based on these three data sets, the values of AUC obtained by GWO–SVM are all higher than those obtained by GA–SVM. Further, by comparing the other metrics of the three classification models, it is found that the classification performance of the two hybrid models is very similar and significantly better than the SVM, which indicates that GWO–SVM, like GA–SVM, can also be used as a reliable model for predicting soil liquefaction potential.

Performance evaluation of hybrid GA–SVM and GWO–SVM models to predict earthquake-induced liquefaction potential of soil: a multi-dataset investigation

Shear-wave velocity (Vs) offers a means to determine the seismic resistance of soil to liquefaction by a fundamental soil property. This paper presents the results of an 11-year international project to gather new Vs site data and develop probabilistic correlations for seismic soil liquefaction occurrence. Toward that objective, shear-wave velocity test sites were identified, and measurements made for 301 new liquefaction field case histories in China, Japan, Taiwan, Greece, and the United States over a decade. The majority of these new case histories reoccupy those previously investigated by penetration testing. These new data are combined with previously published case histories to build a global catalog of 422 case histories of Vs liquefaction performance. Bayesian regression and structural reliability methods facilitate a probabilistic treatment of the Vs catalog for performance-based engineering applications. Where possible, uncertainties of the variables comprising both the seismic demand an...

/pdf/shear-wave-velocity-based-probabilistic-and-deterministic-3f4srp88ew.pdf

Shear-Wave Velocity–Based Probabilistic and Deterministic Assessment of Seismic Soil Liquefaction Potential

https://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29GT.1943-5606.0001390

Erratum for “Shear-Wave Velocity–Based Probabilistic and Deterministic Assessment of Seismic Soil Liquefaction Potential” by R. Kayen, R. E. S. Moss, E. M. Thompson, R. B. Seed, K. O. Cetin, A. Der Kiureghian, Y. Tanaka, and K. Tokimatsu

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Shear-Wave Velocity–Based Probabilistic and Deterministic Assessment of Seismic Soil Liquefaction Potential

Erratum for “Shear-Wave Velocity–Based Probabilistic and Deterministic Assessment of Seismic Soil Liquefaction Potential” by R. Kayen, R. E. S. Moss, E. M. Thompson, R. B. Seed, K. O. Cetin, A. Der Kiureghian, Y. Tanaka, and K. Tokimatsu