Gene expression programming models for liquefaction-induced lateral spreading

Keshavarz, Amin; Tofighi, Hojjat

doi:10.24200/sci.2018.50125.1525

Cited by 3 publications

(2 citation statements)

References 30 publications

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“…In previous studies, AI techniques such as artificial neural networks (ANNs), fuzzy logic, decision tree, and support vector machines (SVM) have been generally used by urban planners to identify the urban evolution pattern or simulation of development alternatives [21][22][23][24][25][26][27][28]. Although GP has been used in different civil engineering disciplines [e.g., [29][30][31][32][33], the use of GP for classification is not yet ubiquitous in civil engineering and urban design [34].…”

Section: Introductionmentioning

confidence: 99%

A new binary genetic programming approach to design public transportation systems according to transit-oriented development criteria

2022

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This study introduces a new evolutionary approach called binary genetic programming (BGP) to design and assess public transportation systems from a sustainable development perspective. The BGP combines evolutionary system identification techniques with k-fold cross-validation to obtain an accurate model between the land use and transportation parameters from a sustainable urban development point of view. To assess the new model, two public transportation systems including the new tram line of Antalya (Turkey) and the bus rapid transit line of Bhopal (India) were considered. The model was applied to classify the transportation systems into transit-oriented development (TOD) and non-TOD. The solutions generated by the new model were compared with those of classic decision tree (DT) as well as the state-of-the-art random forest (RF) models evolved as the benchmarks in this study. The results showed that the BGP is highly efficient and may provide less than 5% classification error. It is superior to the DT and RF solutions, which typically require higher datasets to avoid overfitting. Furthermore, the explicit formulation of BGP in combination with the multicriteria evaluation method increases human insight on the factors affecting the design of public transportations from a sustainable urban development point of view.

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Section: Introductionmentioning

confidence: 99%

A new binary genetic programming approach to design public transportation systems according to transit-oriented development criteria

2022

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“…There are various methods for the representation of GEP output including Karva language (i.e. is the gene language), expression tree (ET) and mathematical function[21][22][23].…”

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confidence: 99%

Prediction of the size of silver nanoparticles prepared via green synthesis: A gene expression programming approach

Sattari

Khayati

2020

Scientia Iranica

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This study presents a new prediction model for estimating the size of silver nanoparticle (AgNPs) prepared by green synthesis via gene expression programming (GEP). Firstly, 30 different experiments were used to construct the GEP models. Plant extract, reaction temperature, concentration of silver nitrate (AgNO 3) and stirring time parameters were considered as input variables and the size of AgNPs parameter selected as output variable. Collected experimentally data randomly divided into 8 testing sets and 22 training sets for further analysis. By consideration of correlation coefficient (R 2), mean absolute error (MAE), root relative square error (RRSE) as criteria, the performance of proposed models by GEP were compared each other. Finally, the best model (i.e., GEP-1) with R 2 =0.9961, MAE=0.2545 and RRSE=0.0668 proposed as a new model with simplified mathematical expressions to estimate the size of AgNPs. The results of sensitivity analysis showed that the amount of plant extract, the concentration of AgNO 3 , stirring time and reaction temperature are the most effective parameters on the size of AgNPs, respectively. Proposed model can be extended for a wide range of applications and provides the possibility of minimum materials consumption for preparation of the lowest size of AgNPs by consideration of practical or economic constraints.

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Intelligent modeling of unconfined compressive strength (UCS) of hybrid cement-modified unsaturated soil with nanostructured quarry fines inclusion

Onyelowe

Jalal

Iqbal

et al. 2021

Innov. Infrastruct. Solut.

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Gene expression programming models for liquefaction-induced lateral spreading

Cited by 3 publications

References 30 publications

A new binary genetic programming approach to design public transportation systems according to transit-oriented development criteria

A new binary genetic programming approach to design public transportation systems according to transit-oriented development criteria

Prediction of the size of silver nanoparticles prepared via green synthesis: A gene expression programming approach

Intelligent modeling of unconfined compressive strength (UCS) of hybrid cement-modified unsaturated soil with nanostructured quarry fines inclusion

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