A neuro-genetic predictive model to approximate overbreak induced by drilling and blasting operation in tunnels

Koopialipoor, Mohammadreza; Armaghani, Danial Jahed; Haghighi, Mojtaba; Ghaleini, Ebrahim Noroozi

doi:10.1007/s10064-017-1116-2

Cited by 128 publications

(49 citation statements)

References 39 publications

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“…A variety of equations, including power, linear, and exponential, were tested for the evaluation and selection of the most appropriate type of equation for the prediction of TS. The equations were evaluated with taking into consideration some prediction intervals (PIs) such as variance accounted for (VAF), root mean square error (RMSE), and R 2 , which have been recommended by lots of researchers such as [24,59,61,63]. In addition, the formulas in regard to such PIs were taken from Mohamad et al's [64] research.…”

Section: Laboratory Experiments and Regression Analysismentioning

confidence: 99%

Invasive Weed Optimization Technique-Based ANN to the Prediction of Rock Tensile Strength

et al. 2019

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In many site investigation phases of civil and mining engineering projects, the tensile strength of the rocks is one of the most significant parameters that must be identified. This parameter can be determined directly through laboratory tests. However, conducting such laboratory tests is costly and time consuming. In this paper, a new artificial neural network (ANN)-based model is developed to predict rock tensile strength, using the invasive weed optimization (IWO) technique. Granite samples for the purpose of this research were selected from a tunnel located in Malaysia and underwent appropriate laboratory tests (i.e., Schmidt hammer, point load, dry density, as well as the Brazilian tensile strength (BTS) as system output). A simple regression analysis was carried out, and the obtained results confirmed the need for developing a model with multiple inputs, rather than one with only a single input, in order to predict BTS values. Aiming to highlight the capability of an IWO-ANN model in estimating BTS, artificial bee colony (ABC)-ANN and imperialism competitive algorithm (ICA)-ANN were also applied and developed. The parameters required for the ANN-based models were obtained using different parametric studies. According to calculated performance indices, a new hybrid IWO-ANN model can provide a higher accuracy level for the prediction of BTS compared to the ABC-ANN and ICA-ANN models. The results showed that the IWO-ANN model is a suitable alternative solution for a robust and reliable engineering design.

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Section: Laboratory Experiments and Regression Analysismentioning

confidence: 99%

Invasive Weed Optimization Technique-Based ANN to the Prediction of Rock Tensile Strength

et al. 2019

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“…The literature consists of various approaches to determining the optimum number of neurons that can exist in the hidden layer. To this end, different researchers have suggested different formulas [104,109,110]. Nevertheless, in the engineering field, numerous studies have implemented various numbers of data, which shows that the intelligent models demonstrate their best performance in every computational space with a certain number of neurons [105,111].…”

Section: Initial Modelmentioning

confidence: 99%

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

et al. 2020

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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.

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“…In this study, four performance indices, namely coefficient of determination (R 2 ), mean absolute error (MAE), root mean square error (RMSE), and variance account for (VAF), were used to measure the prediction performance and accuracy of the developed models. These indices are widely used to assess the accuracy of the developed models in the related studies as well [72][73][74][75][76][77][78][79]. Furthermore, the recently proposed a 20 -index have been used for the evaluation of models.…”

Section: Model's Assessmentmentioning

confidence: 99%

Supervised Machine Learning Techniques to the Prediction of Tunnel Boring Machine Penetration Rate

et al. 2019

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Predicting the penetration rate is a complex and challenging task due to the interaction between the tunnel boring machine (TBM) and the rock mass. Many studies highlight the use of empirical and theoretical techniques in predicting TBM performance. However, reliable performance prediction of TBM is of crucial importance to mining and civil projects as it can minimize the risks associated with capital costs. This study presents new applications of supervised machine learning techniques, i.e., k-nearest neighbor (KNN), chi-squared automatic interaction detection (CHAID), support vector machine (SVM), classification and regression trees (CART) and neural network (NN) in predicting the penetration rate (PR) of a TBM. To achieve this aim, an experimental database was set up, based on field observations and laboratory tests for a tunneling project in Malaysia. In the database, uniaxial compressive strength, Brazilian tensile strength, rock quality designation, weathering zone, thrust force, and revolution per minute were utilized as inputs to predict PR of TBM. Then, KNN, CHAID, SVM, CART, and NN predictive models were developed to select the best one. A simple ranking technique, as well as some performance indices, were calculated for each developed model. According to the obtained results, KNN received the highest-ranking value among all five predictive models and was selected as the best predictive model of this study. It can be concluded that KNN is able to provide high-performance capacity in predicting TBM PR. KNN model identified uniaxial compressive strength (0.2) as the most important and revolution per minutes (0.14) as the least important factor for predicting the TBM penetration rate.

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A neuro-genetic predictive model to approximate overbreak induced by drilling and blasting operation in tunnels

Cited by 128 publications

References 39 publications

Invasive Weed Optimization Technique-Based ANN to the Prediction of Rock Tensile Strength

Invasive Weed Optimization Technique-Based ANN to the Prediction of Rock Tensile Strength

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

Supervised Machine Learning Techniques to the Prediction of Tunnel Boring Machine Penetration Rate

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