Derivation of New Equations for Prediction of Principal Ground-Motion Parameters using M5′ Algorithm

Kaveh, A.; Bakhshpoori, Taha; Hamze‐Ziabari, Seyed Mahmood

doi:10.1080/13632469.2015.1104758

Cited by 28 publications

(10 citation statements)

References 22 publications

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“…The identified significant predictors are moment magnitude of the earthquake, source-to-site distance, the average shear-wave velocity of the site, faulting mechanism, and focal depth. The ML tools utilized in GMPEs include the ANN (the top row in Figure 3) (Bakhshi et al, 2014; Derras et al, 2014; Dhanya and Raghukanth, 2018; Güllü and Erçelebi, 2007; Kerh and Ting, 2005; Khosravikia et al, 2019), genetic programming (GP) (Cabalar and Cevik, 2009), multi-expression programming (MEP) (Alavi et al, 2011), SVR (Tezcan and Cheng, 2012; Thomas et al, 2017), GEP (Güllü, 2012; Javan-Emrooz et al, 2018), Lagrange equation discovery (ED) system (Markič and Stankovski, 2013), conic multivariate adaptive regression splines (CMARS) (Yerlikaya-Ozkurt et al, 2014), randomized adaptive neuro-fuzzy inference system (RANFIS) (Thomas et al, 2016), M5’ model tree and CART (Hamze-Ziabari and Bakhshpoori, 2018; Kaveh et al, 2016), DNN (Derakhshani and Foruzan, 2019), and hybrid methods such as the coupling of GP and orthogonal least squares (OLS) (Gandomi et al, 2011), the combination of ANN and simulated annealing (SA) (Alavi and Gandomi, 2011), the coupling of GP and SA (Mohammadnejad et al, 2012), and the coupling of GA, ANN, and regression analysis (RA) (Akhani et al, 2019).…”

Section: Seismic Hazard Analysismentioning

confidence: 99%

The promise of implementing machine learning in earthquake engineering: A state-of-the-art review

et al. 2020

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Machine learning (ML) has evolved rapidly over recent years with the promise to substantially alter and enhance the role of data science in a variety of disciplines. Compared with traditional approaches, ML offers advantages to handle complex problems, provide computational efficiency, propagate and treat uncertainties, and facilitate decision making. Also, the maturing of ML has led to significant advances in not only the main-stream artificial intelligence (AI) research but also other science and engineering fields, such as material science, bioengineering, construction management, and transportation engineering. This study conducts a comprehensive review of the progress and challenges of implementing ML in the earthquake engineering domain. A hierarchical attribute matrix is adopted to categorize the existing literature based on four traits identified in the field, such as ML method, topic area, data resource, and scale of analysis. The state-of-the-art review indicates to what extent ML has been applied in four topic areas of earthquake engineering, including seismic hazard analysis, system identification and damage detection, seismic fragility assessment, and structural control for earthquake mitigation. Moreover, research challenges and the associated future research needs are discussed, which include embracing the next generation of data sharing and sensor technologies, implementing more advanced ML techniques, and developing physics-guided ML models.

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Section: Seismic Hazard Analysismentioning

confidence: 99%

The promise of implementing machine learning in earthquake engineering: A state-of-the-art review

et al. 2020

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“…Many of these models are highly empirical, and their predictive abilities are limited by the corresponding data sets from which they were derived and do not provide a reliable prediction of maximum transferable load. Recently, soft computing methods have been successfully used to develop predictive models for different problems in civil engineering [8][9][10][11]. In particular, Mansouri and Kisi [7] evaluated the applications of neuro-fuzzy and neural network approaches for estimation of debonding strength for masonry elements retrofitted with FRP composites using eight available experimental datasets consisting of altogether 109 data points.…”

Section: Introductionmentioning

confidence: 99%

New robust formulations for bond strength of FRP reinforcements externally glued on masonry units

Hamze‐Ziabari

Sharahi

2019

SN Appl. Sci.

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The bond interface between fiber-reinforced polymer (FRP) and masonry units is one of the weakest links in tensile strengthened masonry structures. Several empirical formulae have been proposed for estimation of bond strength between FRP reinforcements and masonry units. However, the accuracy of existing formulae for predicting bond strength seems to be significantly limited. In the present study, the M5´ and Multivariate adaptive regression splines approaches are employed to predict the bond strength between FRP reinforcement and masonry units. To develop new models, a comprehensive database including 575 test series (230 distinctive specimens) is collected from different sources in the literature. The newly proposed formulations consider several preeminent parameters involved in the debonding process including the reinforcement width, the ratio between widths of FRP reinforcement and masonry unit, the tensile strength of substrate, the axial strength of reinforcement, and bond length. A comparative study is conducted to evaluate the performances of the developed models against the well-known equations. Results indicated that the proposed models remarkably outperform the existing equations in terms of accuracy. Furthermore, a sensitivity analysis is done to determine the most important parameters in predicting the bond strength. Finally, the safety of different methods is evaluated based on the demerit point classification scale.

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“…The final models of MARS (Eqs. (12) to (15)) are achieved via GCV based on forward selection and backward deletion process. As observed, one of the advantages of MARS algorithm is that not only captures complex relationships between independent and dependent variables but also does not require additional effort to verify a priori assumption for the relationship between them.…”

Section: Basis Function Equationmentioning

confidence: 99%

“…On the other hand, data-driven techniques provide the opportunity to tackle such highly nonlinear prediction problems. These techniques have been interested in many fields and as well applied to civil engineering problems in general engineering such as [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23], and especially in the concrete engineering such as [13,[24][25][26][27][28][29]. Data-driven techniques were also interested for predicting the properties of SCC.…”

Section: Introductionmentioning

confidence: 99%

M5' and Mars Based Prediction Models for Properties of Self-compacting Concrete Containing Fly Ash

Kaveh

Bakhshpoori²,

Hamze‐Ziabari³

2017

Period. Polytech. Civil Eng.

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Derivation of New Equations for Prediction of Principal Ground-Motion Parameters using M5′ Algorithm

Cited by 28 publications

References 22 publications

The promise of implementing machine learning in earthquake engineering: A state-of-the-art review

The promise of implementing machine learning in earthquake engineering: A state-of-the-art review

New robust formulations for bond strength of FRP reinforcements externally glued on masonry units

M5' and Mars Based Prediction Models for Properties of Self-compacting Concrete Containing Fly Ash

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