Liquefaction potential analysis using hybrid multi-objective intelligence model
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ORIGINAL ARTICLE
Liquefaction potential analysis using hybrid multi‑objective intelligence model Abbas Abbaszadeh Shahri1,2 · Fardad Maghsoudi Moud3 Received: 28 February 2020 / Accepted: 5 September 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract Soil liquefaction is one of recognized nonlinear devastating types of ground failures associated with earthquakes. The analyses frameworks for this phenomenon have been addressed using different methods and correlated triggering factors in case histories. In the current paper, a hybrid model using imperialistic competitive metaheuristic algorithm (ICA) incorporated with multi-objective generalized feedforward neural network (MOGFFN) for the purpose of liquefaction potential analysis was assessed. The optimum hybrid ICA-MOGFFN model was applied on a diversified database of 296 compiled case histories comprising nine of the most significant effective parameters on liquefaction. The result of ICA-MOGFFN model demonstrated for 3.01%, 2.09% and 7.46% progress in the success rates for the safety factor, liquefaction occurrence and depth of liquefaction. Accordingly, the conducted precision–recall curves showed 5.08%, 1.73% and 3.92% improvement compared to MOGFFN. Further evaluations using different statistical metrics represented superior progress in performance of hybrid ICA-MOGFFN. The capability of the developed method then was approved from observed agreement with other accepted procedures. The results implied that the developed hybrid model was a flexible and accurate enough tool that can effectively be applied for the liquefaction potential analyses. Using sensitivity analyses, the most and least effective inputs on the predicted liquefaction parameters were identified. Keywords Multi-objective · Hybridizing · Intelligence model · Liquefaction potential · Sensitivity analysis Abbreviations ICA Imperialistic competitive metaheuristic algorithm MOGFFN Multi-objective generalized feedforward neural network LPA Liquefaction potential analysis SPT/CPT Standard/cone penetration tests MLPs Multilayer percepterons γ Unit weight Vs Shear wave velocity FC Fine content CSR Cyclic stress ratio CRR Cyclic resistance ratio
* Abbas Abbaszadeh Shahri [email protected] 1
Department of Civil Engineering, Roudehen Branch, Islamic Azad University, Tehran, Iran
2
Johan Lundberg AB, Uppsala, Sweden
3
ITC Faculty of Geo‑Information Science and Earth Observation, Twente University, Enschede, The Netherlands
amax Maximum acceleration at investigated site σ′v Effective vertical stress rd Stress reduction factor Ncou Number of countries Ncol Number of colonies Nimp Number of imperialists TA/AF/J Training algorithm/activation function/number of neurons in hidden layers RMSEmin Minimum root mean square error
Introduction Liquefactions are earthquake-induced ground failure disasters that due to applied stress during excitations, the soil materials behave like liquid. This process has for the first time been defined by Hazen (1919) as a c
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