Ground Motion Prediction Equations for Malaysia Due to Subduction Zone Earthquakes in Sumatran Region

Liew, Mohd Shahir; Danyaro, Kamaluddeen Usman; Mohamad, Mazlina; Shawn, Lim Eu; Aulov, Aziz

doi:10.1109/access.2017.2748360

Cited by 3 publications

(2 citation statements)

References 56 publications

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“…Other than that, there have been numerous earthquakes in the region that caused tremors on Malaysian soil, such as the tragedy in Aceh in 2004, Nias in 2005 and in March 2012. Malaysia also felt the ground shake due to ruptures in the Indian Ocean on the Australian Plate (Liew, Danyaro, Mohamad, Shawn, & Aulov, 2017).…”

Section: Problem Statementmentioning

confidence: 99%

Is Malaysia Located In The Pacific Ring Of Fire? A Legal Perspective

Sulaiman¹

2019

The European Proceedings of Social and Behavioural Sciences

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Section: Problem Statementmentioning

confidence: 99%

Is Malaysia Located In The Pacific Ring Of Fire? A Legal Perspective

Sulaiman¹

2019

The European Proceedings of Social and Behavioural Sciences

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“…Examples of some of the models of damage indices can be referred in [4]. A lot of initiatives were done to find solutions to the seismic damage prediction including ground motion prediction [5][6][7], seismic assessment [6] (Cotton, 2017), numerical seismic assessment [8] and earthquake magnitude prediction [9][10]. Computational methods such as Support Vector Machine, Neural Network [10][11]), Neural Dynamic Model of Adeli and Park Seismic Model [11], and Adaptive Neuro-Fuzzy Inference System [10] were implemented.…”

mentioning

confidence: 99%

Hybrid backpropagation neural network-particle swarm optimization for seismic damage building prediction

Yusoff¹,

Dnajib

Ismail

2019

IJEECS

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The evaluation of the vulnerability of buildings to earthquakes is of prime importance to ensure a good plan can be generated for the disaster preparedness to civilians. Most of the attempts are directed in calculating the damage index of buildings to determine and predict the vulnerability to certain scales of earthquakes. Most of the solutions used are traditional methods which are time consuming and complex. Some of initiatives have proven that the artificial neural network methods have the potential in solving earthquakes prediction problems. However, these methods have limitations in terms of suffering from local optima, premature convergence and overfitting. To overcome this challenging issue, this paper introduces a new solution to the prediction on the seismic damage index of buildings with the application of hybrid back propagation neural network and particle swarm optimization (BPNN-PSO) method. The prediction was based on damage indices of 35 buildings around Malaysia. The BPNN-PSO demonstrated a better result of 89% accuracy compared to the traditional backpropagation neural network with only 84%. The capability of PSO supports fast convergence method has shown good effort to improve the processing time and accuracy of the results.

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