Overbreak prediction and optimization in tunnel using neural network and bee colony techniques

Koopialipoor, Mohammadreza; Ghaleini, Ebrahim Noroozi; Haghighi, Mojtaba; Kanagarajan, Sujith; Maarefvand, Parviz; Mohamad, Edy Tonnizam

doi:10.1007/s00366-018-0658-7

Cited by 69 publications

(26 citation statements)

References 58 publications

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“…They concluded that the model could be effectively applied to real-life situations. It is true that the ANN is a capable technique for solving/addressing engineering problems/issues; however, they are also subject to certain limitations, including low learning speed and an incapability to escape from local minima [58,59]. To remove these disadvantages, ANN can be integrated with optimization algorithms, such as artificial bee colony (ABC), imperialist competitive algorithm (ICA), invasive weed optimization (IWO), and particle swarm optimization (PSO).…”

Section: Introductionmentioning

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: Introductionmentioning

confidence: 99%

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

et al. 2019

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“…Regarding the performance evaluation of the trained ANN model, Table Ⅱ summarizes some main performance metrics. When using two or more performance indicator(s), the total rank method proposed by Zorlu et al is commonly used to obtain the optimal model from the different results of these indicators [30,[38][39][40].…”

Section: ) Modeling Process Of Annmentioning

confidence: 99%

Application of Artificial Neural Network in Tunnel Engineering: A Systematic Review

Wang

Wei

et al. 2020

IEEE Access

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Due to the lack of living space and the increase in population, there has been a construction boom in the underground space to improve the quality of human life. Tunnel engineering plays a vital role in the development of underground space. In addition to traditional methods, some intelligent methods such as artificial neural networks (ANNs) have been applied to various problems in the tunnel domain in recent years. This paper systematically reviews the application of ANNs from different aspects of tunnel engineering. It reveals that the backpropagation algorithm (BPA) and Levenberg-Marquardt algorithm (LMA) are the most widely used. Due to the limitations of some original models, some scholars use optimization algorithms such as particle swarm optimization (PSO) and genetic algorithm (GA) to optimize the original ANNs to obtain better prediction results. A comparison between the ANN-based methods and methods like statistical methods is conducted. Finally, the following conclusions can be drawn: (1) The recommended ratio of the training set and test set is 3:1; (2) The advantage of optimized ANNs is not apparent when the optimization algorithm varies. Additionally, the performance of ANNs is always better than that of statistical methods.

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“…ABC algorithm has been used in different engineering problems including well placement optimization of petroleum reservoirs [43], optimization of water discharge in dams [44], data classification [45], and machine scheduling [46]. More description on the ABC algorithm can be found in other references [47][48][49][50]. A typical flowchart of ABC algorithm is shown in Figure 12 6.…”

Section: General Description Of Artificial Bee Colonymentioning

confidence: 99%

Applications of Artificial Intelligence Techniques in Optimizing Drilling

Koopialipoor¹,

Noorbakhsh²

2020

Emerging Trends in Mechatronics

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Artificial intelligence has transformed the industrial operations. One of the important applications of artificial intelligence is reducing the computational costs of optimization. Various algorithms based on their assumptions to solve problems have been presented and investigated, each of which having assumptions to solve the problems. In this chapter, firstly, the concept of optimization is fully explained. Then, an artificial bee colony (ABC) algorithm is used on a case study in the drilling industry. This algorithm optimizes the problem of study in combination with ANN modeling. At the end, various models are fully developed and discussed. The results of the algorithm show that by better understanding the drilling data, the conditions can be improved.

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Overbreak prediction and optimization in tunnel using neural network and bee colony techniques

Cited by 69 publications

References 58 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

Application of Artificial Neural Network in Tunnel Engineering: A Systematic Review

Applications of Artificial Intelligence Techniques in Optimizing Drilling

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