GEP Tree-Based Prediction Model for Interfacial Bond Strength of Externally Bonded FRP Laminates on Grooves with Concrete Prism

Amin, Muhammad; Iqbal, Mudassir; Jamal, Arshad; Ullah, Shahid; Khan, Kaffayatullah; Abu-Arab, Abdullah M.; Al-Ahmad, Qasem M. S.; Khan, Sikandar

doi:10.3390/polym14102016

Cited by 14 publications

(16 citation statements)

References 54 publications

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“…The average R 2 value for this model is the highest (0.928). A value of R 2 greater than 0.8 shows close agreement of experimental and predicted results [ 78 ]. The corresponding values for RSME and mean MAE (0.776) are also the lowest, indicating the robustness and superior prediction performance of the M11 models.…”

Section: Resultssupporting

confidence: 54%

Predicting Bond Strength between FRP Rebars and Concrete by Deploying Gene Expression Programming Model

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Rebars made of fiber-reinforced plastic (FRP) might be the future reinforcing material, replacing mild steel rebars, which are prone to corrosion. The bond characteristics of FRP rebars differ from those of mild steel rebars due to their different stress-strain behavior than mild steel. As a result, determining the bond strength (BS) qualities of FRP rebars is critical. In this work, BS data for FRP rebars was investigated, utilizing non-linear capabilities of gene expression programming (GEP) on 273 samples. The BS of FRP and concrete was considered a function of bar surface (Bs), bar diameter (db), concrete compressive strength (fc′), concrete-cover-bar-diameter ratio (c/d), and embedment-length-bar-diameter ratio (l/d). The investigation of the variable number of genetic parameters such as number of chromosomes, head size, and number of genes was undertaken such that 11 different models (M1–M11) were created. The results of accuracy evaluation parameters, namely coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE) imply that the M11 model outperforms other created models for the training and testing stages, with values of (0.925, 0.751, 1.08) and (0.9285, 0.802, 1.11), respectively. The values of R2 and error indices showed that there is very close agreement between the experimental and predicted results. 30 number chromosomes, 9 head size, and 5 genes yielded the optimum model. The parametric analysis revealed that db, c/d, and l/d significantly affected the BS. The FRP rebar diameter size is greater than 10 mm, whereas a l/d ratio of more than 12 showed a considerable decrease in BS. In contrast, the rise in c/d ratio revealed second-degree increasing trend of BS.

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Section: Resultssupporting

confidence: 54%

Predicting Bond Strength between FRP Rebars and Concrete by Deploying Gene Expression Programming Model

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“…GEP has been successfully employed for tackling numerous complicated engineering challenges due to a number of its inherent advantages [ 41 ]. The GEP algorithm has been successfully used for a variety of concrete-related applications [ 42 , 43 , 44 , 45 , 46 , 47 ]. GEP uses a population-based technique, which is inspired by the traditional genetic algorithms (GAs) procedure for prediction and optimal solution finding.…”

Section: Methodsmentioning

confidence: 99%

“…Because of the intricacy of the output’s mathematical equation, the number of genes has a significant impact on the model’s performance. Previously, majority of researchers employed different of genes, i.e., 3 [ 49 ], 4 [ 42 ], and 5 [ 47 ]. Increasing the number of genes may boost performance, but it will also complicate the output’s mathematical equation, and thus the number of genes in this analysis ranged from 3 to 5.…”

Section: Methodsmentioning

confidence: 99%

Prediction Models for Estimating Compressive Strength of Concrete Made of Manufactured Sand Using Gene Expression Programming Model

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The depletion of natural resources of river sand and its availability issues as a construction material compelled the researchers to use manufactured sand. This study investigates the compressive strength of concrete made of manufactured sand as a partial replacement of normal sand. The prediction model, i.e., gene expression programming (GEP), was used to estimate the compressive strength of manufactured sand concrete (MSC). A database comprising 275 experimental results based on 11 input variables and 1 target variable was used to train and validate the developed models. For this purpose, the compressive strength of cement, tensile strength of cement, curing age, Dmax of crushed stone, stone powder content, fineness modulus of the sand, water-to-binder ratio, water-to-cement ratio, water content, sand ratio, and slump were taken as input variables. The investigation of a varying number of genetic characteristics, such as chromosomal number, head size, and gene number, resulted in the creation of 11 alternative models (M1-M11). The M5 model outperformed other created models for the training and testing stages, with values of (4.538, 3.216, 0.919) and (4.953, 3.348, 0.906), respectively, according to the results of the accuracy evaluation parameters root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). The R2 and error indices values revealed that the experimental and projected findings are in extremely close agreement. The best model has 200 chromosomes, 8 head sizes, and 3 genes. The mathematical expression achieved from the GEP model revealed that six parameters, namely the compressive and tensile strength of cement, curing period, water–binder ratio, water–cement ratio, and stone powder content contributed effectively among the 11 input variables. The sensitivity analysis showed that water–cement ratio (46.22%), curing period (25.43%), and stone powder content (13.55%) were revealed as the most influential variables, in descending order. The sensitivity of the remaining variables was recorded as w/b (11.37%) > fce (2.35%) > fct (1.35%).

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“…The use of fiber-reinforced polymer (FRP) bars, sheets, and strips to enhance RC or even steel structural components is one of the prevalent approaches for these kinds of repairs. Because of the corrosion of traditional steel reinforcement that leads to deterioration, RC structures require periodic maintenance and repair [ 1 , 2 , 3 ]. As a result, to meet the strength requirements and revised code designs, old structures are being strengthened as a newly emerging construction activity [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

Investigating the Bond Strength of FRP Laminates with Concrete Using LIGHT GBM and SHAPASH Analysis

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The corrosion of steel reinforcement necessitates regular maintenance and repair of a variety of reinforced concrete structures. Retrofitting of beams, joints, columns, and slabs frequently involves the use of fiber-reinforced polymer (FRP) laminates. In order to develop simple prediction models for calculating the interfacial bond strength (IBS) of FRP laminates on a concrete prism containing grooves, this research evaluated the nonlinear capabilities of three ensemble methods—namely, random forest (RF) regression, extreme gradient boosting (XGBoost), and Light Gradient Boosting Machine (LIGHT GBM) models—based on machine learning (ML). In the present study, the IBS was the desired variable, while the model comprised five input parameters: elastic modulus x thickness of FRP (EfTf), width of FRP plate (bf), concrete compressive strength (fc′), width of groove (bg), and depth of groove (hg). The optimal parameters for each ensemble model were selected based on trial-and-error methods. The aforementioned models were trained on 70% of the entire dataset, while the remaining data (i.e., 30%) were used for the validation of the developed models. The evaluation was conducted on the basis of reliable accuracy indices. The minimum value of correlation of determination (R2 = 0.82) was observed for the testing data of the RF regression model. In contrast, the highest (R2 = 0.942) was obtained for LIGHT GBM for the training data. Overall, the three models showed robust performance in terms of correlation and error evaluation; however, the trend of accuracy was obtained as follows: LIGHT GBM > XGBoost > RF regression. Owing to the superior performance of LIGHT GBM, it may be considered a reliable ML prediction technique for computing the bond strength of FRP laminates and concrete prisms. The performance of the models was further supplemented by comparing the slopes of regression lines between the observed and predicted values, along with error analysis (i.e., mean absolute error (MAE), and root-mean-square error (RMSE)), predicted-to-experimental ratio, and Taylor diagrams. Moreover, the SHAPASH analysis revealed that the elastic modulus x thickness of FRP and width of FRP plate are the factors most responsible for IBS in FRP.

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GEP Tree-Based Prediction Model for Interfacial Bond Strength of Externally Bonded FRP Laminates on Grooves with Concrete Prism

Cited by 14 publications

References 54 publications

Predicting Bond Strength between FRP Rebars and Concrete by Deploying Gene Expression Programming Model

Predicting Bond Strength between FRP Rebars and Concrete by Deploying Gene Expression Programming Model

Prediction Models for Estimating Compressive Strength of Concrete Made of Manufactured Sand Using Gene Expression Programming Model

Investigating the Bond Strength of FRP Laminates with Concrete Using LIGHT GBM and SHAPASH Analysis

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