Krill herd algorithm-based neural network in structural seismic reliability evaluation

Asteris, Panagiotis G.; Nozhati, Saeed; Nikoo, Mehdi; Cavaleri, Liborio; Nikoo, Mohammad Reza

doi:10.1080/15376494.2018.1430874

Cited by 113 publications

(43 citation statements)

References 33 publications

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“…A trained ANN has learned to rapidly map a given input into the desired output quantities (similar to curve fitting procedures) and thereby can be used as a meta-model enhancing the computational efficiency of a numerical analysis process. This major advantage of a trained ANN over conventional numerical analysis procedures, such as regression analysis, under the condition that the training and validation data cover the entire range of input parameters values, is that the results can be produced with much less computational effort [93,[177][178][179][180][181][182][183][184][185][186].…”

Section: Failure Criterion Based On Artificial Neural Networkmentioning

confidence: 99%

Stochastic Vulnerability Assessment of Masonry Structures: Concepts, Modeling and Restoration Aspects

et al. 2019

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A methodology aiming to predict the vulnerability of masonry structures under seismic action is presented herein. Masonry structures, among which many are cultural heritage assets, present high vulnerability under earthquake. Reliable simulations of their response to seismic stresses are exceedingly difficult because of the complexity of the structural system and the anisotropic and brittle behavior of the masonry materials. Furthermore, the majority of the parameters involved in the problem such as the masonry material mechanical characteristics and earthquake loading characteristics have a stochastic-probabilistic nature. Within this framework, a detailed analytical methodological approach for assessing the seismic vulnerability of masonry historical and monumental structures is presented, taking into account the probabilistic nature of the input parameters by means of analytically determining fragility curves. The emerged methodology is presented in detail through application on theoretical and built cultural heritage real masonry structures. leading to a final approval (or rejection) of the decisions already taken for repair or strengthening of the existing structure. Step 8: Explanatory ReportThe last step, as a result of the proposed methodology, includes the detailed "Explanatory Report", where all the collected information, the diagnosis, including the safety evaluation, and any decision to intervene should be fully detailed. This document is essential for eventual future analyses and interventions' measures in the structure. Computational and Mathematical AspectsIn this section, the most basic analytical constitutive laws and numerical models required for the successful implementation of the proposed methodology are presented in detail. In particular, the finite element model for the macro-modeling of masonry structures, the failure criteria, the damage indices, the performance levels and the mathematical background of fragility curves are presented.

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Section: Failure Criterion Based On Artificial Neural Networkmentioning

confidence: 99%

Stochastic Vulnerability Assessment of Masonry Structures: Concepts, Modeling and Restoration Aspects

et al. 2019

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“…where P p is accuracy and P exp are the expected agreements. RMSE is often used to evaluate the differences between the predicted and target values [57][58][59][60][61][62], it can be calculated using the following equation:…”

Section: Validation Criteriamentioning

confidence: 99%

GIS Based Novel Hybrid Computational Intelligence Models for Mapping Landslide Susceptibility: A Case Study at Da Lat City, Vietnam

Nguyen

Tran

et al. 2019

Sustainability

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Landslides affect properties and the lives of a large number of people in many hilly parts of Vietnam and in the world. Damages caused by landslides can be reduced by understanding distribution, nature, mechanisms and causes of landslides with the help of model studies for better planning and risk management of the area. Development of landslide susceptibility maps is one of the main steps in landslide management. In this study, the main objective is to develop GIS based hybrid computational intelligence models to generate landslide susceptibility maps of the Da Lat province, which is one of the landslide prone regions of Vietnam. Novel hybrid models of alternating decision trees (ADT) with various ensemble methods, namely bagging, dagging, MultiBoostAB, and RealAdaBoost, were developed namely B-ADT, D-ADT, MBAB-ADT, RAB-ADT, respectively. Data of 72 past landslide events was used in conjunction with 11 landslide conditioning factors (curvature, distance from geological boundaries, elevation, land use, Normalized Difference Vegetation Index (NDVI), relief amplitude, stream density, slope, lithology, weathering crust and soil) in the development and validation of the models. Area under the receiver operating characteristic (ROC) curve (AUC), and several statistical measures were applied to validate these models. Results indicated that performance of all the models was good (AUC value greater than 0.8) but B-ADT model performed the best (AUC= 0.856). Landslide susceptibility maps generated using the proposed models would be helpful to decision makers in the risk management for land use planning and infrastructure development.

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“…Also, over the last decades, ANNs have appeared as efficient meta-modelling methods applicable to a wide range of sciences, including material science and structural engineering [30][31][32]. An important characteristic of ANNs is that they can be used to build soft sensors, i.e., models with the ability to estimate critical quantities without having to measure them [30].…”

Section: Methodsmentioning

confidence: 99%

Neural Prediction of Tunnels’ Support Pressure in Elasto-Plastic, Strain-Softening Rock Mass

2018

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Featured Application: This work can be conjunctly used with the support characteristic curve of circular tunnels to find the optimum time of the installation of the support system in a way to restrict the displacements to a specific value. The approach described in this manuscript facilitates the design of circular tunnels for elasto-plastic, strain-softening rock masses obeying both Mohr-Coulomb and Hoek-Brown strength criteria. Abstract:The prediction of the support pressure (P i ) and the development of the ground reaction curve (GRC) are crucial elements of the convergence-confinement procedure used to design underground structures. In this paper, two different types of artificial neural networks (ANNs) are used to predict the P i of circular tunnels in elasto-plastic, strain-softening rock mass. The developed ANNs consider the stress state, the radial displacement of tunnel and the material softening behavior. Among these parameters, strain softening is the parameter of the deterioration of the material's strength in the plastic zone. The analysis also presents separate solutions for the Mohr-Coulomb and Hoek-Brown strength criteria. In this regard, multi-layer perceptron (MLP) and radial basis function (RBF) ANNs were successfully applied. MLP with the architectures of 15-5-10-1 for the Mohr-Coulomb criteria and 17-5-15-1 for the Hoek-Brown criteria appeared optimum for the prediction of the P i . On the other hand, the RBF networks with the architectures of 15-5-1 for the Mohr-Coulomb criterion and 17-3-12-1 for the Hoek-Brown criterion were found to be the optimum for the prediction of the P i .

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Krill herd algorithm-based neural network in structural seismic reliability evaluation

Cited by 113 publications

References 33 publications

Stochastic Vulnerability Assessment of Masonry Structures: Concepts, Modeling and Restoration Aspects

Stochastic Vulnerability Assessment of Masonry Structures: Concepts, Modeling and Restoration Aspects

GIS Based Novel Hybrid Computational Intelligence Models for Mapping Landslide Susceptibility: A Case Study at Da Lat City, Vietnam

Neural Prediction of Tunnels’ Support Pressure in Elasto-Plastic, Strain-Softening Rock Mass

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