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Fragility functions of blockwork wharves using artificial neural networks
Abstract The use of artificial neural networks in the general framework of a performance-based seismic vulnerability evaluation for earth retaining structures is presented. A blockwork wharf-foundation-backfill complex is modeled with advanced nonlinear 2D finite difference software, wherein liquefaction occurrence is explicitly accounted for. A simulation algorithm is adopted to sample geotechnical input parameters according to their statistical distribution, and extensive time histories analyses are then performed for several earthquake intensity levels. In the process, the seismic input is also considered as a random variable. A large dataset of virtual realizations of the behavior of different configurations under recorded ground motions is thus obtained, and an artificial neural network is implemented in order to find the unknown nonlinear relationships between seismic and geotechnical input data versus the expected performance of the facility. After this process, fragility curves are systematically derived by applying Monte Carlo simulation on the obtained correlations. The novel fragility functions herein proposed for blockwork wharves take into account different geometries, liquefaction occurrence and type of failure mechanism. Results confirm that the detrimental effects of liquefaction increase the probability of failure at all damage states. Moreover, it is also demonstrated that increasing the base width/height ratio results in higher failure probabilities for the horizontal sliding than for the tilting towards the sea.
Highlights A particular typology of gravity quay wall is studied. Advanced numerical analyses are performed to assess its seismic performance. Artificial Neural Networks (ANNs) are implemented using the results of the analyses. Fragility curves are obtained using Monte Carlo simulations and results from ANNs.
Fragility functions of blockwork wharves using artificial neural networks
Abstract The use of artificial neural networks in the general framework of a performance-based seismic vulnerability evaluation for earth retaining structures is presented. A blockwork wharf-foundation-backfill complex is modeled with advanced nonlinear 2D finite difference software, wherein liquefaction occurrence is explicitly accounted for. A simulation algorithm is adopted to sample geotechnical input parameters according to their statistical distribution, and extensive time histories analyses are then performed for several earthquake intensity levels. In the process, the seismic input is also considered as a random variable. A large dataset of virtual realizations of the behavior of different configurations under recorded ground motions is thus obtained, and an artificial neural network is implemented in order to find the unknown nonlinear relationships between seismic and geotechnical input data versus the expected performance of the facility. After this process, fragility curves are systematically derived by applying Monte Carlo simulation on the obtained correlations. The novel fragility functions herein proposed for blockwork wharves take into account different geometries, liquefaction occurrence and type of failure mechanism. Results confirm that the detrimental effects of liquefaction increase the probability of failure at all damage states. Moreover, it is also demonstrated that increasing the base width/height ratio results in higher failure probabilities for the horizontal sliding than for the tilting towards the sea.
Highlights A particular typology of gravity quay wall is studied. Advanced numerical analyses are performed to assess its seismic performance. Artificial Neural Networks (ANNs) are implemented using the results of the analyses. Fragility curves are obtained using Monte Carlo simulations and results from ANNs.
Fragility functions of blockwork wharves using artificial neural networks
Calabrese, Armando (author) / Lai, Carlo G. (author)
Soil Dynamics and Earthquake Engineering ; 52 ; 88-102
2013-05-18
15 pages
Article (Journal)
Electronic Resource
English
ANN , artificial neural network , CDF , cumulative density function , CPT , cone penetration test , DS , damage state , EDP , engineering demand parameter , FEM , finite element method , GM , ground motion , IA , Arias intensity , IM , intensity measure , INGV , Italian Institute of Geophysics and Volcanology , MCS , Monte Carlo simulation , MLE , maximum likelihood estimation , pdf , probability density function , PGA , peak ground acceleration , PGD , peak ground displacement , PGV , peak ground velocity , PIANC , International Navigation Association , PSHA , probabilistic seismic hazard analysis , RHD , residual horizontal displacement , SASW , spectral analysis of surface waves , UHS , uniform hazard spectrum , Seaport , Gravity walls , Blockwork wharves , Artificial Neural Networks (ANN) , Backpropagation , Monte Carlo Simulation (MCS) , Fragility curves
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