Prediction of the Strength of Rubberized Concrete by an Evolved Random Forest Model

In this research, multiexpression programming (MEP) has been employed to model the compressive strength, splitting tensile strength, and flexural strength of waste sugarcane bagasse ash (SCBA) concrete. Particle swarm optimization (PSO) algorithm was used to fine-tune the hyperparameter of the proposed MEP. The formulation of SCBA concrete was correlated with five input parameters. To train and test the proposed model, a large number of data were collected from the published literature. Afterward, waste SCBA was collected, processed, and characterized for partial replacement of cement in concrete. Concrete specimens with varying proportion of SCBA were prepared in the laboratory, and results were used for model validation. The performance of the developed models was then evaluated by statistical criteria and error assessment tests. The result shows that the performance of MEP with PSO algorithm significantly enhanced its accuracy. The essential input variables affecting the output were revealed, and the parametric analysis confirms that the models are accurate and have captured the essential properties of SCBA. Finally, the cross validation ensured the generalized capacity and robustness of the models. Hence, the adopted approach, i.e., MEP-based modeling with PSO, could be an effective tool for accurate modeling of the concrete properties, thus directly contributing to the construction sector by consuming waste and protecting the environment.

Section: Introductionmentioning

confidence: 99%

Machine Learning‐Based Modeling with Optimization Algorithm for Predicting Mechanical Properties of Sustainable Concrete

Shah

Memon

Niazi

et al. 2021

“…e present study aims to propose a robust machine learning technique to be used as a tool to predict the permeability of pervious concrete. An efficient global optimization algorithm (called the beetle antennae search, BAS) proposed by Jiang et al was adopted in this study to obtain the optimized parameters of RF [37]. In this way, the random forest (RF) and beetle antennae search (BAS) algorithms were combined to build a robust machine learning technique, named as BRF method.…”

Section: Research Objective and Overviewmentioning

confidence: 99%

“…However, no corresponding studies were reported to use RF to predict the permeability of pervious concrete so far. Besides, as far as the RF model employed in the previous studies, the hyperparameters were still required to be optimized to arrive at their optimized predictive ability [37].…”

Section: Introductionmentioning

confidence: 99%

Predicting the Permeability of Pervious Concrete Based on the Beetle Antennae Search Algorithm and Random Forest Model

Huang

Duan

Zhang

et al. 2020

Pervious concrete is an environmentally friendly material that improves water permeability, skid resistance, and sound absorption characteristics. Permeability is the most important functional performance for the pervious concrete while limited studies have been conducted to predict permeability based on mix-design parameters. This study proposed a method to combine the beetle antennae search (BAS) and random forest (RF) algorithm to predict the permeability of pervious concrete. Based on the 36 samples designed in the laboratory and 4 key influencing variables, the permeability of pervious concrete can be obtained by varying mix-design parameters by RF. BAS algorithm was used to tune the hyperparameters of RF, which were then verified by the so-called 10-fold cross-validation. Furthermore, the model to combine the BAS and RF was validated by the correlation parameters. The results showed that the hyperparameters of RF can be tuned by the BAS efficiently; the BAS can combine the conventional RF algorithm to construct the evolved model to predict the permeability of pervious concrete; the cement/aggregate ratio was the most significant variable to determine the permeability, followed by the coarse aggregate proportions.

“…Recently, machine learning methods have been widely used for predicting the mechanical properties of construction materials [12,[17][18][19][20][21][22][23]. e assessment of the strength of CPB by artificial intelligence methods has also been presented.…”

Section: Introductionmentioning

confidence: 99%

Development of an Ensemble Intelligent Model for Assessing the Strength of Cemented Paste Backfill

Sun

Zhang

et al. 2020

Self Cite

Cemented paste backfill (CPB) is an eco-friendly composite containing mine waste or tailings and has been widely used as construction materials in underground stopes. In the field, the uniaxial compressive strength (UCS) of CPB is critical as it is closely related to the stability of stopes. Predicting the UCS of CPB using traditional mathematical models is far from being satisfactory due to the highly nonlinear relationships between the UCS and a large number of influencing variables. To solve this problem, this study uses a support vector machine (SVM) to predict the UCS of CPB. The hyperparameters of the SVM model are tuned using the beetle antennae search (BAS) algorithm; then, the model is called BSVM. The BSVM is then trained on a dataset collected from the experimental results. To explain the importance of each input variable on the UCS of CPB, the variable importance is obtained using a sensitivity study with the BSVM as the objective function. The results show that the proposed BSVM has high prediction accuracy on the test set with a high correlation coefficient (0.97) and low root-mean-square error (0.27 MPa). The proposed model can guide the design of CPB during mining.