This research presents a new model for finding optimal conditions in the concrete technology area. To do that, results of a series of laboratory investigations on concrete samples were considered and used to design several artificial intelligence (AI) models. The data samples include 8 parameters i.e., silica fume replacement ratio, fly ash replacement ratio, fine aggregate, water content, high rate water reducing agent, coarse aggregate, total cementitious material, and age of samples, were used to predict and optimize the compressive strength of concrete samples. For optimization purposes, this study used a human learning optimization (HLO) algorithm to find the optimal results as well as optimizing the kernel coefficients of the support vector regression (SVR) models. Initially, to form the core of this research, various models were constructed and proposed to design the required relationship between the data using SVR. Since different SVR kernels have their own coefficients, using optimization theory, the probability of error in the models was reduced and the models were identified and executed with the highest accuracy. Finally, the polynomial model was selected as the model with the lowest computational error and the highest accuracy for evaluating the compressive strength of the concrete samples. The accuracy of the proposed SVR model for training and testing data was obtained as the coefficient of determination (R2) = 0.9694 and R2 = 0.9470, respectively. This function was considered as a relation, to be developed by the HLO algorithm to find optimal options under different conditions. The results for 14 samples, which are the most important examples of this research, showed that the optimal states are obtained with a high level of accuracy. This confirms the proper use/develop of the SVR-HLO algorithm in designing the predictive model as well as finding optimal conditions in the concrete technology area.

Reduction of computational error by optimizing SVR kernel coefficients to simulate concrete compressive strength through the use of a human learning optimization algorithm

When designing flat slabs made of steel fiber-reinforced concrete (SFRC), it is very important to predict their punching shear capacity accurately. The use of machine learning seems to be a great way to improve the accuracy of empirical equations currently used in this field. Accordingly, this study utilized tree predictive models (i.e., random forest (RF), random tree (RT), and classification and regression trees (CART)) as well as a novel feature selection (FS) technique to introduce a new model capable of estimating the punching shear capacity of the SFRC flat slabs. Furthermore, to automatically create the structure of the predictive models, the current study employed a sequential algorithm of the FS model. In order to perform the training stage for the proposed models, a dataset consisting of 140 samples with six influential components (i.e., the depth of the slab, the effective depth of the slab, the length of the column, the compressive strength of the concrete, the reinforcement ratio, and the fiber volume) were collected from the relevant literature. Afterward, the sequential FS models were trained and verified using the above-mentioned database. To evaluate the accuracy of the proposed models for both testing and training datasets, various statistical indices, including the coefficient of determination (R2) and root mean square error (RMSE), were utilized. The results obtained from the experiments indicated that the FS-RT model outperformed FS-RF and FS-CART models in terms of prediction accuracy. The range of R2 and RMSE values were obtained as 0.9476-0.9831 and 14.4965-24.9310, respectively; in this regard, the FS-RT hybrid technique demonstrated the best performance. It was concluded that the three hybrid techniques proposed in this paper, i.e., FS-RT, FS-RF, and FS-CART, could be applied to predicting SFRC flat slabs.

https://www.mdpi.com/1996-1944/13/17/3902/pdf

A Novel Feature Selection Approach Based on Tree Models for Evaluating the Punching Shear Capacity of Steel Fiber-Reinforced Concrete Flat Slabs

This research focuses on presenting new models based on classifiers that can be applied to various problems. Adaboost is a type of ensemble learning machine that uses classifiers that contain a range of base models. This study used enhanced Adaboost models to classify soil types base on tree algorithm models that are less commonly used in this area. Determining the type of soil in different geotechnical projects is very important. Using soil classification, soil properties such as mechanical properties, performance against static and dynamic loads can be found. Regarding the importance of the subject, 440 samples of the actual project were used to design this new methodology. The dataset included clay content, moisture content, specific gravity, void ratio, plastic, and liquid limit parameters to determine the type of soil classification. These samples were tested with high precision and the actual type of classification was obtained. For comparison, two enhanced tree and neural network model were designed and developed according to these conditions. The results of this classification were presented for different soil samples. The developed adaboost model showed that it could well classify the soil. This model showed that only 11 samples were not correctly identified among the total data (88 data). Therefore, this new technique can be used to increase the accuracy and reduce the cost of projects.

A novel approach for classification of soils based on laboratory tests using Adaboost, Tree and ANN modeling

Most mining and tunneling projects usually require blasting operations to remove rock mass. Previous studies have mentioned that if the blasting operation is not properly designed, it may lead to several environmental issues, such as ground vibration. This study presents various machine learning (ML) techniques, i.e., hybrid extreme learning machines (ELMs) with the grasshopper optimization algorithm (GOA) and Harris hawks optimization (HHO) for controlling and predicting ground vibrations resulting from mine blasting. Actually, the GOA–ELM and HHO–ELM models are improved versions of a previously developed ELM model, and they are able to provide higher performance capacity. For the proposed ML modeling, a database was established consisting of 166 datasets collected from Malaysian quarries. The efficacy of the proposed ML techniques was observed in the training stage as well as in the testing stage, and the results were evaluated against five parameters constituting the fitness criteria. The results showed that the GOA–ELM model delivered more accurate ground vibration values compared to the HHO–ELM model. The system error values of the GOA–ELM model for the training and testing datasets were 2.0239 and 2.8551, respectively. The coefficients of determination of the GOA-ELM model for the training and testing datasets were 0.9410 and 0.9105, respectively. It was concluded that the new hybrid model is able to forecast ground vibration resulting from mine blasting with high level of accuracy. The capabilities of this hybrid model can be extended further to mitigate other environmental issues caused by mine blasting.

Optimal ELM–Harris Hawks Optimization and ELM–Grasshopper Optimization Models to Forecast Peak Particle Velocity Resulting from Mine Blasting

Accurate and reliable predictions of rock deformations are crucial in many rock-based projects in civil and mining engineering. In this research, a new system for the prediction of rock deformation was developed using various machine learning models, including multi-layer perceptron (MLP), the k-nearest neighbors (KNN), random forest (RF), and tree. The optimum model developed in this research was designed using a stacking-tree-RF-KNN-MLP structure. The developed structure consolidates different characteristics of four different models with the aim of increasing the prediction accuracy of the Young’s modulus. Each of the basic models has various influential parameters that affect the performance of the final system. By optimizing each of these parameters, the stacking-tree-RF-KNN-MLP system was refined to obtain the final model. In this research rock deformations were predicted using four index tests, including porosity, point load strength, Schmidt hammer and p-wave velocity. The stack-tree-KNN-RF-MLP model developed in this research, registered the highest prediction accuracy (R 2 = 0.8197, MSE = 227.371, RMSE = 15.079 and MAE = 12.123). The developed model may be refined over an extended database. 

Introducing stacking machine learning approaches for the prediction of rock deformation

Seepage is one of the most challenging issues in some procedures such as design, construction, and operation of embankment or earth fill dams. The purpose of this research is to develop a new solution based on governing equations to solve the seepage problem in an effective way. Therefore, by implementing the equations in the programming environment, more than 24,000 models were designed to be applicable to different conditions. Input data included different parameters such as slopes in upstream and downstream, embankment width, soil permeability coefficient, height, and freeboard. With the use of this big data, a new process was developed to provide simple mathematical models for the seepage rate analysis. The study first used intelligent models to simulate the seepage behavior. Finally, the accuracy of the models was optimized using a new metaheuristic algorithm. This led to the ultimate flexibility of the final model presented as a new solution capable of evaluating different conditions. Finally, using the best model, new mathematical relationships were developed based on this methodology. This new solution can be used as a proper alternative to the governing equations of seepage rate estimation. Another advantage of the proposed model is its high flexibility that can be well applied to engineering design in this field, which was not possible using the initial equations.

Dongchun Tang

Papers

Seepage Analysis in Short Embankments Using Developing a Metaheuristic Method Based on Governing Equations