A hybrid computational approach to derive new ground-motion prediction equations

Gandomi, Amir H.; Alavi, Amir H.; Mousavi, Mehdi; Tabatabaei, Seyed Morteza

doi:10.1016/j.engappai.2011.01.005

Cited by 118 publications

(32 citation statements)

References 31 publications

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“…An efficient approach to prevent overfitting is to test the derived models on a validation set [6,21]. This approach was used in this study for improving the generalization of the models.…”

Section: Data Preprocessingmentioning

confidence: 99%

“…The validation data were used to designate the generalization capability of the models on data they did not learn on (model selection). Since both of the learning and validation data were involved in the modeling process, they were categorized into one group referred to as ''training data'' [21]. The models with the best performance on both of the learning and validation data sets were finally selected as the outcomes of the runs.…”

Section: Data Preprocessingmentioning

confidence: 99%

“…The amount of data used for the training process is an important issue for verifying reliability of final models [7,21]. To cope with this limitation, Frank and Todeschini [22] stated that the minimum ratio of the number of objects over the number of selected variables for model acceptability is 3.…”

Section: Data Preprocessingmentioning

confidence: 99%

“…High R values and low MAE values result in lowering Obj and indicate a more precise model. In addition, the above function considers the effects of different data divisions for the training data [21].…”

Section: Performance Measuresmentioning

confidence: 99%

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A robust predictive model for base shear of steel frame structures using a hybrid genetic programming and simulated annealing method

Aminian

Javid

Asghari

et al. 2011

Neural Comput & Applic

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This study presents a new empirical model to estimate the base shear of plane steel structures subjected to earthquake load using a hybrid method integrating genetic programming (GP) and simulated annealing (SA), called GP/SA. The base shear of steel frames was formulated in terms of the number of bays, number of storey, soil type, and situation of braced or unbraced. A classical GP model was developed to benchmark the GP/SA model. The comprehensive database used for the development of the correlations was obtained from finite element analysis. A parametric analysis was carried out to evaluate the sensitivity of the base shear to the variation of the influencing parameters. The GP/SA and classical GP correlations provide a better prediction performance than the widely used UBC code and a neural network-based model found in the literature. The developed correlations may be used as quick checks on solutions developed by deterministic analyses.

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“…An efficient approach to prevent overfitting is to test the derived models on a validation set [6,21]. This approach was used in this study for improving the generalization of the models.…”

Section: Data Preprocessingmentioning

confidence: 99%

Section: Data Preprocessingmentioning

confidence: 99%

Section: Data Preprocessingmentioning

confidence: 99%

Section: Performance Measuresmentioning

confidence: 99%

See 2 more Smart Citations

A robust predictive model for base shear of steel frame structures using a hybrid genetic programming and simulated annealing method

Aminian

Javid

Asghari

et al. 2011

Neural Comput & Applic

Self Cite

View full text Add to dashboard Cite

show abstract

“…In another study by Alavi et al 19 , a high precision model was derived to predict the flow number of dense asphalt mixtures using a novel hybrid method coupling GP and simulated annealing. Gandomi et al 20 also developed a novel hybrid method coupling GP and orthogonal least squares. In another paper by Gandomi and Alavi 21 , a new approach for behavioural modelling of structural engineering systems is presented using a promising variant of GP, namely multigene GP (MGGP).…”

Section: Recent Genetic Programming Studies Carried Out In Pavement Ementioning

confidence: 99%

Rutting prediction of asphalt mixtures modified by polypropylene fibers via repeated creep testing by utilising genetic programming

et al. 2013

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A novel application of genetic programming (GP) for modelling and presenting closed form solutions to the rutting prediction for polypropylene (PP) modified asphalt mixtures is investigated. Various PP fibers have been utilised for bitumen modification and repeated creep (RC) tests have been carried out. Marshall specimens, fabricated with multifilament 3 mm (M-03) type PP fibers at optimum bitumen content of 5% have been tested under different load values and patterns at 50 °C to investigate their rutting potential. It has been shown that the service lives of PP fiber-reinforced Marshall specimens are respectively longer than the control specimens under the same testing conditions (5 to 12 times). Input variables in the developed GP model use the physical properties of Marshall specimens such as PP type, specimen height, unit weight, voids in mineral aggregate, voids filled with asphalt, air voids, rest period and pulse counts. The performance of the accuracy of the proposed GP model is observed to be quite satisfactory. To obtain the main effects plot, detailed parametric studies have been performed. The presented closed form solution will also help further researchers willing to perform studies on the prediction of the rutting potential of asphalt without carrying out destructive tests for similar type of aggregate sources, bitumen, aggregate gradation, modification technique and laboratory conditions.

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Probabilistic loss assessment of curved bridges considering the effect of ground motion directionality

Feng

Yuan

Sextos

2021

Earthq Engng Struct Dyn

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This study aims to assess, in a probabilistic manner, the effect of seismic excitation direction on the performance of a 638 m‐long, 12 span prestressed concrete bridge with significant curvature in plan on the basis of overall monetary repair loss. By means of a large number of nonlinear dynamic analyses, the optimum intensity measure (IM) was selected from a pool of twenty candidates considering the impact of seismic excitation direction. Next, the variability of bridge loss with respect to the seismic incidence angle under different groups of earthquake intensities was obtained. Finally, the applicability of combination rules for the seismic performance of curved bridges was evaluated based on the multidirectional loss assessment results. Meanwhile, the effects of seismic excitation direction and earthquake intensity on the evaluation of combination rules were also revealed. Results indicate that velocity‐related or/and structural‐dependent IMs are more appropriate for the probabilistic seismic demand analysis of curved bridges. It is also notable that the total bridge loss gradually turns out to be independent of the seismic excitation direction as the seismic intensity increases and the damage develops throughout the structure. Additionally, the widely used 100/30 and 100/40 rules may lead to an unconservative estimation for the loss of the curved bridge. The above findings highlight the necessity of addressing the issue of ground motion directionality in a rigorous probabilistic manner and pave the way for further research on qualifying the influences of geometric configuration, material parameters, soil type and the consequent soil‐structure interaction effect.

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A hybrid computational approach to derive new ground-motion prediction equations

Cited by 118 publications

References 31 publications

A robust predictive model for base shear of steel frame structures using a hybrid genetic programming and simulated annealing method

A robust predictive model for base shear of steel frame structures using a hybrid genetic programming and simulated annealing method

Rutting prediction of asphalt mixtures modified by polypropylene fibers via repeated creep testing by utilising genetic programming

Probabilistic loss assessment of curved bridges considering the effect of ground motion directionality

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