Developing a new intelligent technique to predict overbreak in tunnels using an artificial bee colony-based ANN

Koopialipoor, Mohammadreza; Ghaleini, Ebrahim Noroozi; Tootoonchi, Hossein; Armaghani, Danial Jahed; Haghighi, Mojtaba; Hedayat, Ahmadreza

doi:10.1007/s12665-019-8163-x

Cited by 71 publications

(22 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…If yes, the repetitions end; if not, it goes back to the second step. For more detailed information about ABC and the way it operates, one may refer to literature (e.g., [62,80]). A flowchart of ABC is depicted in Figure 4.…”

Section: Artificial Bee Colonymentioning

confidence: 99%

“…This system was also applied to the results obtained from the other hybrid models developed in this paper. The application of this method can be found in some research [62,80]. This technique is fully explained by Zorlu et al [101], based on which a rank value was allocated to each PI in its group (training and testing).…”

Section: Ica-annmentioning

confidence: 99%

“…This system was also applied to the results obtained from the other hybrid models developed in this paper. The application of this method can be found in some research [62,80].…”

Section: Ica-annmentioning

confidence: 99%

See 2 more Smart Citations

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

et al. 2019

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Artificial Bee Colonymentioning

confidence: 99%

Section: Ica-annmentioning

confidence: 99%

See 1 more Smart Citation

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

et al. 2019

Self Cite

View full text Add to dashboard Cite

show abstract

“…In recent years, artificial intelligence (AI) technology has been widely developed and applied in many areas for solving special problems such as the prediction of blast-induced rock movement [14], flyrock distance [15,16], overbreak [17,18], air overpressure [19,20], rockburst [21][22][23], mechanical properties of material [24][25][26][27][28][29], tunnel convergence [30] and tunnel boring machine penetration rate [31]. In practice, AI technology is also applied to predict blast-induced ground vibration and yield high prediction precision [32][33][34].…”

Section: Introductionmentioning

confidence: 99%

Effective Assessment of Blast-Induced Ground Vibration Using an Optimized Random Forest Model Based on a Harris Hawks Optimization Algorithm

Shi

Zhou

et al. 2020

Applied Sciences

View full text Add to dashboard Cite

Most mines choose the drilling and blasting method which has the characteristics of being a cheap and efficient method to fragment rock mass, but blast-induced ground vibration damages the surrounding rock mass and structure and is a drawback. To predict, analyze and control the blast-induced ground vibration, the random forest (RF) model, Harris hawks optimization (HHO) algorithm and Monte Carlo simulation approach were utilized. A database consisting of 137 datasets was collected at different locations around the Tonglvshan open-cast mine, China. Seven variables were selected and collected as the input variables, and peak particle velocity was chosen as the output variable. At first, an RF model and a hybrid model, namely a HHO-RF model, were developed, and the prediction results checked by 3 performance indices to show that the proposed HHO-RF model can provide higher prediction performance. Then blast-induced ground vibration was simulated by using the Monte Carlo simulation approach and the developed HHO-RF model. After analyzing, the mean peak particle velocity value was 0.98 cm/s, and the peak particle velocity value did not exceed 1.95 cm/s with a probability of 90%. The research results of this study provided a simple, accurate method and basis for predicting, evaluating blast-induced ground vibration and optimizing the blast design before blast operation.

show abstract

“…Artificial neural network (ANN) is considered to be an AI technique that is able to forecast almost all problems in science and engineering fields [11][12][13][14][15][16][17][18][19][20][21]. However, they have several limitations which discussed and introduced in previous research [22][23][24]. As stated in several references [25][26][27], the use of efficient optimization algorithms (OAs) can overcome these limitations.…”

Section: Introductionmentioning

confidence: 99%

Assessing Dynamic Conditions of the Retaining Wall: Developing Two Hybrid Intelligent Models

et al. 2019

Self Cite

View full text Add to dashboard Cite

The precise estimation and forecast of the safety factor (SF) in civil engineering applications is considered as an important issue to reduce engineering risk. The present research investigates new artificial intelligence (AI) techniques for the prediction of SF values of retaining walls, as important and resistant structures for ground forces. These structures have complicated performances in dynamic conditions. Consequently, more than 8000 designs of these structures were dynamically evaluated. Two AI models, namely the imperialist competitive algorithm (ICA)-artificial neural network (ANN), and the genetic algorithm (GA)-ANN were used for the forecasting of SF values. In order to design intelligent models, parameters i.e., the wall thickness, stone density, wall height, soil density, and internal soil friction angle were examined under different dynamic conditions and assigned as inputs to predict SF of retaining walls. Various models of these systems were constructed and compared with each other to obtain the best one. Results of models indicated that although both hybrid models are able to predict SF values with a high accuracy and they can be introduced as new models in the field, the retaining wall performance could be properly predicted in dynamic conditions using the ICA-ANN model. Under these conditions, a combination of engineering design and artificial intelligence techniques can be used to control and secure retaining walls in dynamic conditions.

show abstract

Developing a new intelligent technique to predict overbreak in tunnels using an artificial bee colony-based ANN

Cited by 71 publications

References 44 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

Effective Assessment of Blast-Induced Ground Vibration Using an Optimized Random Forest Model Based on a Harris Hawks Optimization Algorithm

Assessing Dynamic Conditions of the Retaining Wall: Developing Two Hybrid Intelligent Models

Contact Info

Product

Resources

About