Application of soft computing methods for predicting the elastic modulus of recycled aggregate concrete

Golafshani, Emadaldin Mohammadi; Behnood, Ali

doi:10.1016/j.jclepro.2017.11.186

Cited by 163 publications

(68 citation statements)

References 60 publications

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“…Figure 5 shows the comparison of the shear strength between the ML predictions and the experimental measurements. It is noteworthy that lower RMSE and MAPE and higher R 2 values indicate a better accuracy of the model prediction [ 48 ]. Figure 6 compares the results of the performance indicators for the two ML-based approaches and the previously evaluated methods.…”

Section: Shear Resistance Evaluation Using Machine Learning Approamentioning

confidence: 99%

“…The widely accepted ML approaches include, among many others, artificial neural network, kernel methods like support vector machine, and decision trees like random forest [ 41 , 42 , 43 ]. These models have been effectively used as predictive modeling approaches for normal concrete (e.g., [ 44 ]) as well as recycled concrete [ 45 , 46 , 47 , 48 ].…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning-Based Evaluation of Shear Capacity of Recycled Aggregate Concrete Beams

Yong

Zhao

et al. 2020

Materials

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Recycled aggregate concrete (RAC) is a promising solution to address the challenges raised by concrete production. However, the current lack of pertinent design rules has led to a hesitance to accept structural members made with RAC. It would entail even more difficulties when facing application scenarios where brittle failure is possible (e.g., beam in shear). In this paper, existing major shear design formulae established primarily for conventional concrete beams were assessed for RAC beams. Results showed that when applied to the shear test database compiled for RAC beams, those formulae provided only inaccurate estimations with surprisingly large scatter. To cope with this bias, machine learning (ML) techniques deemed as potential alternative predictors were resorted to. First, a Grey Relational Analysis (GRA) was carried out to rank the importance of the parameters that would affect the shear capacity of RAC beams. Then, two contemporary ML approaches, namely, the artificial neural network (ANN) and the random forest (RF), were leveraged to simulate the beams’ shear strength. It was found that both models produced even better predictions than the evaluated formulae. With this superiority, a parametric study was undertaken to observe the trends of how the parameters played roles in influencing the shear resistance of RAC beams. The findings indicated that, though less influential than the structural parameters such as shear span ratio, the effect of the replacement ratio of recycled aggregate (RA) was still significant. Nevertheless, the value of vc/(fc)1/2 (i.e., the shear contribution from RAC normalized with respect to the square root of its strength) predicted by the ML-based approaches appeared to be insignificantly affected by the replacement level. Given the existing inevitable large experimental scatter, more shear tests are certainly needed and, for safe application of RAC, using partial factors calibrated to consider the uncertainty is feasible when designing the shear strength of RAC beams. Some suggestions for future works are also given at the end of this paper.

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Section: Shear Resistance Evaluation Using Machine Learning Approamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Evaluation of Shear Capacity of Recycled Aggregate Concrete Beams

Yong

Zhao

et al. 2020

Materials

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“…It was conducted on the basis of the commonly known coefficient of determination (R 2 ), root-mean-square error (RMSE), and mean absolute error (MAE) [22,24,27,28,[40][41][42][43][44][45][46][47].…”

Section: Performance Evaluationmentioning

confidence: 99%

Neural Prediction of Tunnels’ Support Pressure in Elasto-Plastic, Strain-Softening Rock Mass

2018

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Featured Application: This work can be conjunctly used with the support characteristic curve of circular tunnels to find the optimum time of the installation of the support system in a way to restrict the displacements to a specific value. The approach described in this manuscript facilitates the design of circular tunnels for elasto-plastic, strain-softening rock masses obeying both Mohr-Coulomb and Hoek-Brown strength criteria. Abstract:The prediction of the support pressure (P i ) and the development of the ground reaction curve (GRC) are crucial elements of the convergence-confinement procedure used to design underground structures. In this paper, two different types of artificial neural networks (ANNs) are used to predict the P i of circular tunnels in elasto-plastic, strain-softening rock mass. The developed ANNs consider the stress state, the radial displacement of tunnel and the material softening behavior. Among these parameters, strain softening is the parameter of the deterioration of the material's strength in the plastic zone. The analysis also presents separate solutions for the Mohr-Coulomb and Hoek-Brown strength criteria. In this regard, multi-layer perceptron (MLP) and radial basis function (RBF) ANNs were successfully applied. MLP with the architectures of 15-5-10-1 for the Mohr-Coulomb criteria and 17-5-15-1 for the Hoek-Brown criteria appeared optimum for the prediction of the P i . On the other hand, the RBF networks with the architectures of 15-5-1 for the Mohr-Coulomb criterion and 17-3-12-1 for the Hoek-Brown criterion were found to be the optimum for the prediction of the P i .

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“…The SVM has been used to solve various problems involving predicting the compressive strength of different types of concrete [28][29][30][31] and jet-grouted material [32]. Apart from estimating compressive strength, the SVM is used for several applications in engineering, such as traffic sign detection [33], modelling soil pollution [34], predicting daily flow of river [35] predicting elastic modulus of concrete [36], modelling landslide susceptibility [37], air balancing for ventilation systems [38] and predicting shear force for base isolation device [39,40]. Its other successful applications include for example, classifying building information modelling elements [41], system reliability analysis of slopes [42], estimation of concrete expansion caused by alkali-aggregate reaction [43], damage detection in a three-story frame structure [44], prediction of lateral load capacity of piles [45], crack inspection for aircraft skin [46] and assessing liquefaction potential [47].…”

Section: Introductionmentioning

confidence: 99%

Support vector machine for determining the compressive strength of brick-mortar masonry using NDT data fusion (case study: Kharagpur, India)

2019

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The accurate prediction of compressive strength of brick-mortar masonry walls is crucial for the damage assessment of load-bearing masonry constructions. Direct tests conducted to estimate compressive strength involve core drilling and are expensive. To estimate compressive strength, several indirect test parameters can be used as empirical predictors. Nondestructive tests can be rapidly executed, can significantly reduce repair costs, and can increase the knowledge level of buildings by indirectly estimating compressive strength. This study aimed to determine the compressive strength of masonry construction by using support vector machines (SVMs). Input variables of the model are test data obtained from the nondestructive and destructive testing of 44 masonry wallettes cast in a laboratory for evaluating the compressive strength of brick (f b), rebound hammer number, and ultrasonic pulse velocity, while the compressive strength of the wall (f c) is output. The final results obtained using an SVM model are validated for a masonry building in Kharagpur, India through experimental testing, and these results are compared with other established empirical relationships. The results indicate that the SVM can be efficiently used to predict the compressive strength of brick-mortar masonry.

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Application of soft computing methods for predicting the elastic modulus of recycled aggregate concrete

Cited by 163 publications

References 60 publications

Machine Learning-Based Evaluation of Shear Capacity of Recycled Aggregate Concrete Beams

Machine Learning-Based Evaluation of Shear Capacity of Recycled Aggregate Concrete Beams

Neural Prediction of Tunnels’ Support Pressure in Elasto-Plastic, Strain-Softening Rock Mass

Support vector machine for determining the compressive strength of brick-mortar masonry using NDT data fusion (case study: Kharagpur, India)

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