New Ground-Motion Prediction Equations Using Multi Expression Programing

Alavi, Amir H.; Gandomi, Amir H.; Modaresnezhad, Minoo; Mousavi, Mehdi

doi:10.1080/13632469.2010.526752

Cited by 46 publications

(15 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The γ nn value for the PGA S is 0.067%, and for the PGV S it is 0.70%. Even though the trend line for the PGD S δ values is zero, the standard deviations range from 0.27 to 0.71 natural log units, which are significant when compared with the standard deviations of 0.825 and 0.90 natural log units calculated by Campbell and Bozorgnia (2008) and Alavi et al (2011). Figure 4 shows that even though there is no bias in the prediction of PGD S between the two analysis methods, the difference in the predicted value of PGD S could be significant.…”

Section: Comparison Of the Difference Between Ela And Nlamentioning

confidence: 69%

Comparison of Equivalent Linear and Nonlinear Site Response Analysis Results and Model to Estimate Maximum Shear Strain

Carlton,

Tokimatsu

2016

Earthquake Spectra

View full text Add to dashboard Cite

We compared the results of equivalent linear (ELA) and nonlinear site response analyses (NLA) and found that the differences between the values of the peak ground acceleration ( PGA), peak ground velocity ( PGV), Arias intensity ( I a), significant duration ( D5–75), and response spectrum for periods between 0.025 s and 2 s predicted by each method are non-negligible for maximum shear strain values predicted by ELA ( γ max, ELA) greater than 0.04% to 1.0%. As γ max, ELA increases, ELA in general predict smaller shear strain and D5–75 values, and larger PGA, PGV, I a, mean period, and response spectral values for periods less than 0.1 s and periods near the natural site period than NLA. To help researchers and practitioners decide when to use ELA and/or NLA, we developed a model to estimate γ max, ELA before conducting a site response analysis.

show abstract

Section: Comparison Of the Difference Between Ela And Nlamentioning

confidence: 69%

Comparison of Equivalent Linear and Nonlinear Site Response Analysis Results and Model to Estimate Maximum Shear Strain

Carlton,

Tokimatsu

2016

Earthquake Spectra

View full text Add to dashboard Cite

show abstract

“…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%

“…These issues have been tackled when a comprehensive ground motion database became available, namely, the NGA project strong motion database (Chiou et al, 2008), which contains 3551 recorded motion data from 173 shallow crustal earthquakes, ranging in magnitude from 4.2 to 7.9. The following enhancements have been made using the NGA motion database: (1) a well-constructed learning, validation, and testing structure to avoid overfitting (Alavi et al, 2011; Alavi and Gandomi, 2011); (2) a separate testing procedure on a different motion database to verify the generalization capability of the model (Gandomi et al, 2011; Thomas et al, 2016, 2017); (3) a hybrid framework that couples two or three ML tools to significantly improve the model performance (Akhani et al, 2019; Alavi and Gandomi, 2011; Gandomi et al, 2011; Hamze-Ziabari and Bakhshpoori, 2018; Mohammadnejad et al, 2012; Thomas et al, 2016). Moreover, the newly compiled NGA-West2 strong motion database (Ancheta et al, 2014), consisting of 21,336 recordings from 599 shallow crustal earthquakes, has provided a promising resource for developing more complex soft computing models.…”

Section: Seismic Hazard Analysismentioning

confidence: 99%

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

et al. 2020

View full text Add to dashboard Cite

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.

show abstract

“…Genetic Programming (GP) is an alternative approach that can overcome this limitation [22,23]. GP generates simplified prediction equations without assuming a prior form of the relationship [24][25][26][27][28][29]. This method has been successfully applied to the behavioral modeling of FRP concrete.…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Model for the Prediction of Bond Strength of FRP Bars in Concrete: A Soft Computing Approach

et al. 2019

View full text Add to dashboard Cite

Accurate prediction of bond behavior of fiber reinforcement polymer (FRP) concrete has a pivotal role in the construction industry. This paper presents a soft computing method called multi-gene genetic programming (MGGP) to develop an intelligent prediction model for the bond strength of FRP bars in concrete. The main advantage of the MGGP method over other similar methods is that it can formulate the bond strength by combining the capabilities of both standard genetic programming and classical regression. A number of parameters affecting the bond strength of FRP bars were identified and fed into the MGGP algorithm. The algorithm was trained using an experimental database including 223 test results collected from the literature. The proposed MGGP model accurately predicts the bond strength of FRP bars in concrete. The newly defined predictor variables were found to be efficient in characterizing the bond strength. The derived equation has better performance than the widely-used American Concrete Institute (ACI) model.

show abstract

New Ground-Motion Prediction Equations Using Multi Expression Programing

Cited by 46 publications

References 25 publications

Comparison of Equivalent Linear and Nonlinear Site Response Analysis Results and Model to Estimate Maximum Shear Strain

Comparison of Equivalent Linear and Nonlinear Site Response Analysis Results and Model to Estimate Maximum Shear Strain

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

An Intelligent Model for the Prediction of Bond Strength of FRP Bars in Concrete: A Soft Computing Approach

Contact Info

Product

Resources

About