Development of Evaluation Framework for the Unconfined Compressive Strength of Soils Based on the Fundamental Soil Parameters Using Gene Expression Programming and Deep Learning Methods

Bodour, Wassel Al; Hanandeh, Shadi; Hajij, Mustafa; Murad, Yasmin

doi:10.1061/(asce)mt.1943-5533.0004087

Cited by 24 publications

(8 citation statements)

References 25 publications

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“…Neural network investigation confirms the ability to ascertain pavement evaluation indices (Eldin and Senouci, 1995). Lima et al (2019) assessed the abilities of (Gopalakrishnan et al, 2017;Hanandeh et al, 2020aHanandeh et al, , 2020bYou et al, 2020;Piryonesi and El-Diraby, 2021;Ye et al, 2021;Zuhair Murad, 2021;Al Bodour et al, 2022;Hanandeh, 2022). Barzegaran et al (2021) used IRI prediction models from pavement distresses which could be an alternative solution.…”

Section: Iri ( P )mentioning

confidence: 93%

Application of Soft Computing for Estimation of Pavement Condition Indicators and Predictive Modeling

Hanandeh

Hanandeh²,

Alhiary³

et al. 2022

Front. Built Environ.

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The pavement management system is recognized as an assertive discipline that works on pavement indices to predict the pavement performance condition. This study used soft computing methods such as genetic algorithms and artificial intelligence to propose a modern generation of pavement indices for road networks in Jordan. The datasets used in this study were collected from multiple roads in Jordan, and 128 data points were used in this study. The input variables are the pavement condition index (PCI) and the international roughness index (IRI) in the artificial neural network (ANN) and gene expression programming (GEP) models. The output variable is the pavement serviceability rate (PSR). The results show an efficient performance benefit of using these techniques. In addition, the ANN and GEP models were able to predict the output variable with a reasonable accuracy, where the ANN model has an R2 value of 0.95, 0.87, and 0.98 for the PCI, IRI, and PSR, respectively. The (R2) values of the GEP model are 0.94, 0.89, and 0.99 for PCI, IRI, and PSR, respectively.

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Section: Iri ( P )mentioning

confidence: 93%

Application of Soft Computing for Estimation of Pavement Condition Indicators and Predictive Modeling

Hanandeh

Hanandeh²,

Alhiary³

et al. 2022

Front. Built Environ.

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“…Similarly, recent studies have also discussed similar behavior [34][35][36][37][38][39][40]. The adopted procedure for evaluating each of the variables has been wildly utilized in research [41][42][43][44][45][46][47][48][49][50][51][52][53][54][55].…”

Section: Effect Of Shear Span To Depth Ratio (A/d)mentioning

confidence: 99%

Robust Prediction of Shear Strength of SFRC Using Artificial Neural Networks

Odeh

Alawadi²

2022

Sustainability

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The assessment of shear behavior in SFRC beams is a complex problem that depends on several parameters. This research aims to develop an artificial neural network (ANN) model that has six inputs nodes that represent the fiber volume (Vf), fiber factor (F), shear span to depth ratio (a/d), reinforcement ratio (ρ), effective depth (d), and concrete compressive strength (fc′) to predict shear capacity of steel fiber-reinforced concrete beams, using 241 data test gathered from previous researchers. The proposed ANN model provides a good implementation and superior accuracy for predicting shear strength compared to previous literature, with a Root Mean Square Error (RMSE) of 0.87, the average ratio (vtest/vpredicted) of 1.00, and the coefficient of variation of 22%. It was shown from parametric analysis the reinforcement ratio and shear span to depth ratio contributed the most impact on the shear strength. It can also be noticed that all parameters have a nearly linear impact on the shear strength except the shear span to depth ratio has an exponential effect.

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“…Gene expression programming (GEP) is the modified form of GP and is widely appreciated by the researchers in the field of civil engineering [29][30][31][32][33][34]. For instance, Jalal et al, [35] developed prediction models for the assessment of compaction characteristics of expansive soils using GEP.…”

Section: Basics Of Genetic Programming (Gp) and Gene Expression Progr...mentioning

confidence: 99%

A robust prediction model for evaluation of plastic limit based on sieve # 200 passing material using gene expression programming

et al. 2022

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This study aims to propose a novel and high-accuracy prediction model of plastic limit (PL) based on soil particles passing through sieve # 200 (0.075 mm) using gene expression programming (GEP). PL is used for the classification of fine-grained soils which are particles passing from sieve # 200. However, it is conventionally evaluated using sieve # 40 passing material. According to literature, PL should be determined using sieve # 200 passing material. Although, PL200 is considered the accurate representation of plasticity of soil, its’ determination in laboratory is time consuming and difficult task. Additionally, it is influenced by clay and silt content along with sand particles. Thus, artificial intelligence-based techniques are considered viable solution to propose the prediction model which can incorporate multiple influencing parameters. In this regard, the laboratory experimental data was utilized to develop prediction model for PL200 using gene expression programming considering sand, clay, silt and PL using sieve 40 material (PL40) as input parameters. The prediction model was validated through multiple statistical checks such as correlation coefficient (R2), root mean square error (RMSE), mean absolute error (MAE) and relatively squared error (RSE). The sensitivity and parametric studies were also performed to further justify the accuracy and reliability of the proposed model. The results show that the model meets all of the criteria and can be used in the field.

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Development of Evaluation Framework for the Unconfined Compressive Strength of Soils Based on the Fundamental Soil Parameters Using Gene Expression Programming and Deep Learning Methods

Cited by 24 publications

References 25 publications

Application of Soft Computing for Estimation of Pavement Condition Indicators and Predictive Modeling

Application of Soft Computing for Estimation of Pavement Condition Indicators and Predictive Modeling

Robust Prediction of Shear Strength of SFRC Using Artificial Neural Networks

A robust prediction model for evaluation of plastic limit based on sieve # 200 passing material using gene expression programming

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