A comparative study on the application of various artificial neural networks to simultaneous prediction of rock fragmentation and backbreak
- Sayadi, Ahmad, Monjezi, Masoud, Talebi, Nemat, Khandelwal, Manoj
- Authors: Sayadi, Ahmad , Monjezi, Masoud , Talebi, Nemat , Khandelwal, Manoj
- Date: 2013
- Type: Text , Journal article
- Relation: Journal of Rock Mechanics and Geotechnical Engineering Vol. 5, no. 4 (2013), p. 318-324
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- Description: In blasting operation, the aim is to achieve proper fragmentation and to avoid undesirable events such as backbreak. Therefore, predicting rock fragmentation and backbreak is very important to arrive at a technically and economically successful outcome. Since many parameters affect the blasting results in a complicated mechanism, employment of robust methods such as artificial neural network may be very useful. In this regard, this paper attends to simultaneous prediction of rock fragmentation and backbreak in the blasting operation of Tehran Cement Company limestone mines in Iran. Back propagation neural network (BPNN) and radial basis function neural network (RBFNN) are adopted for the simulation. Also, regression analysis is performed between independent and dependent variables. For the BPNN modeling, a network with architecture 6-10-2 is found to be optimum whereas for the RBFNN, architecture 6-36-2 with spread factor of 0.79 provides maximum prediction aptitude. Performance comparison of the developed models is fulfilled using value account for (VAF), root mean square error (RMSE), determination coefficient (R2) and maximum relative error (MRE). As such, it is observed that the BPNN model is the most preferable model providing maximum accuracy and minimum error. Also, sensitivity analysis shows that inputs burden and stemming are the most effective parameters on the outputs fragmentation and backbreak, respectively. On the other hand, for both of the outputs, specific charge is the least effective parameter. © 2013 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences.
- Authors: Sayadi, Ahmad , Monjezi, Masoud , Talebi, Nemat , Khandelwal, Manoj
- Date: 2013
- Type: Text , Journal article
- Relation: Journal of Rock Mechanics and Geotechnical Engineering Vol. 5, no. 4 (2013), p. 318-324
- Full Text:
- Reviewed:
- Description: In blasting operation, the aim is to achieve proper fragmentation and to avoid undesirable events such as backbreak. Therefore, predicting rock fragmentation and backbreak is very important to arrive at a technically and economically successful outcome. Since many parameters affect the blasting results in a complicated mechanism, employment of robust methods such as artificial neural network may be very useful. In this regard, this paper attends to simultaneous prediction of rock fragmentation and backbreak in the blasting operation of Tehran Cement Company limestone mines in Iran. Back propagation neural network (BPNN) and radial basis function neural network (RBFNN) are adopted for the simulation. Also, regression analysis is performed between independent and dependent variables. For the BPNN modeling, a network with architecture 6-10-2 is found to be optimum whereas for the RBFNN, architecture 6-36-2 with spread factor of 0.79 provides maximum prediction aptitude. Performance comparison of the developed models is fulfilled using value account for (VAF), root mean square error (RMSE), determination coefficient (R2) and maximum relative error (MRE). As such, it is observed that the BPNN model is the most preferable model providing maximum accuracy and minimum error. Also, sensitivity analysis shows that inputs burden and stemming are the most effective parameters on the outputs fragmentation and backbreak, respectively. On the other hand, for both of the outputs, specific charge is the least effective parameter. © 2013 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences.
An ANN-based approach to predict blast-induced ground vibration of Gol-E-Gohar iron ore mine, Iran
- Saadat, Mahdi, Khandelwal, Manoj, Monjezi, Masoud
- Authors: Saadat, Mahdi , Khandelwal, Manoj , Monjezi, Masoud
- Date: 2014
- Type: Text , Journal article
- Relation: Journal of Rock Mechanics and Geotechnical Engineering Vol. 6, no. 1 (2014), p. 67-76
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- Description: Blast-induced ground vibration is one of the inevitable outcomes of blasting in mining projects and may cause substantial damage to rock mass as well as nearby structures and human beings. In this paper, an attempt has been made to present an application of artificial neural network (ANN) to predict the blast-induced ground vibration of the Gol-E-Gohar (GEG) iron mine, Iran. A four-layer feed-forward back propagation multi-layer perceptron (MLP) was used and trained with Levenberg-Marquardt algorithm. To construct ANN models, the maximum charge per delay, distance from blasting face to monitoring point, stemming and hole depth were taken as inputs, whereas peak particle velocity (PPV) was considered as an output parameter. A database consisting of 69 data sets recorded at strategic and vulnerable locations of GEG iron mine was used to train and test the generalization capability of ANN models. Coefficient of determination (R2) and mean square error (MSE) were chosen as the indicators of the performance of the networks. A network with architecture 4-11-5-1 and R2 of 0.957 and MSE of 0.000722 was found to be optimum. To demonstrate the supremacy of ANN approach, the same 69 data sets were used for the prediction of PPV with four common empirical models as well as multiple linear regression (MLR) analysis. The results revealed that the proposed ANN approach performs better than empirical and MLR models. © 2013 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences.
A borehole stability study by newly designed laboratory tests on thick-walled hollow cylinders
- Hashemi, Sam, Melkoumian, Nouné, Taheri, Abbas
- Authors: Hashemi, Sam , Melkoumian, Nouné , Taheri, Abbas
- Date: 2015
- Type: Text , Journal article
- Relation: Journal of Rock Mechanics and Geotechnical Engineering Vol. 7, no. 5 (2015), p. 519-531
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- Description: At several mineral exploration drilling sites in Australia, weakly consolidated formations mainly consist of sand particles that are poorly bonded by cementing agents such as clay, iron oxide cement or calcite. These formations are being encountered when drilling boreholes to the depth of up to 200 m. To study the behaviour of these materials, thick-walled hollow cylinder (TWHC) and solid cylindrical synthetic specimens were designed and prepared by adding Portland cement and water to sand grains. The effects of different parameters such as water and cement contents, grain size distribution and mixture curing time on the characteristics of the samples were studied to identify the mixture closely resembling the formation at the drilling site. The Hoek triaxial cell was modified to allow the visual monitoring of grain debonding and borehole breakout processes during the laboratory tests. The results showed the significance of real-time visual monitoring in determining the initiation of the borehole breakout. The size-scale effect study on TWHC specimens revealed that with the increasing borehole size, the ductility of the specimen decreases, however, the axial and lateral stiffnesses of the TWHC specimen remain unchanged. Under different confining pressures the lateral strain at the initiation point of borehole breakout is considerably lower in a larger size borehole (20 mm) compared to that in a smaller one (10 mm). Also, it was observed that the level of peak strength increment in TWHC specimens decreases with the increasing confining pressure. © 2015 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences.
- Authors: Hashemi, Sam , Melkoumian, Nouné , Taheri, Abbas
- Date: 2015
- Type: Text , Journal article
- Relation: Journal of Rock Mechanics and Geotechnical Engineering Vol. 7, no. 5 (2015), p. 519-531
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- Reviewed:
- Description: At several mineral exploration drilling sites in Australia, weakly consolidated formations mainly consist of sand particles that are poorly bonded by cementing agents such as clay, iron oxide cement or calcite. These formations are being encountered when drilling boreholes to the depth of up to 200 m. To study the behaviour of these materials, thick-walled hollow cylinder (TWHC) and solid cylindrical synthetic specimens were designed and prepared by adding Portland cement and water to sand grains. The effects of different parameters such as water and cement contents, grain size distribution and mixture curing time on the characteristics of the samples were studied to identify the mixture closely resembling the formation at the drilling site. The Hoek triaxial cell was modified to allow the visual monitoring of grain debonding and borehole breakout processes during the laboratory tests. The results showed the significance of real-time visual monitoring in determining the initiation of the borehole breakout. The size-scale effect study on TWHC specimens revealed that with the increasing borehole size, the ductility of the specimen decreases, however, the axial and lateral stiffnesses of the TWHC specimen remain unchanged. Under different confining pressures the lateral strain at the initiation point of borehole breakout is considerably lower in a larger size borehole (20 mm) compared to that in a smaller one (10 mm). Also, it was observed that the level of peak strength increment in TWHC specimens decreases with the increasing confining pressure. © 2015 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences.
Optimization of cement-based grouts using chemical additives
- Azadi, Mohammadreza, Taghichian, Ali, Taheri, Ali
- Authors: Azadi, Mohammadreza , Taghichian, Ali , Taheri, Ali
- Date: 2017
- Type: Text , Journal article
- Relation: Journal of Rock Mechanics and Geotechnical Engineering Vol. 9, no. 4 (2017), p. 623-637
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- Description: Grout injection is used for sealing or strengthening the ground in order to prevent water entrance or any failure after excavation. There are many methods of grouting. Permeation grouting is one of the most common types in which the grout material is injected to the pore spaces of the ground. In grouting operations, the grout quality is important to achieve the best results. There are four main characteristics for a grout mixture including bleeding, setting time, strength, and viscosity. In this paper, we try to build some efficient grouting mixtures with different water to cement ratios considering these characteristics. The ingredients of grout mixtures built in this study are cement, water, bentonite, and some chemical additives such as sodium silicate, sodium carbonate, and triethanolamine (TEA). The grout mixtures are prepared for both of the sealing and strengthening purposes for a structural project. Effect of each above-mentioned ingredient is profoundly investigated. Since each ingredient may have positive or negative aspect, an optimization of appropriate amount of each ingredient is determined. The optimization is based on 200 grout mixture samples with different percentages of ingredients. Finally, some of these grout mixtures are chosen for the introduced project. It should be mentioned that grouting operations depend on various factors such as pressure of injection, ground structure and grain size of soils. However, quality of a grout can be helpful to make an injection easier and reasonable. For example, during the injection, a wrong estimated setting time can destroy the injected grout by washing the grout or setting early which prevents grouting. This paper tries to show some tests in easy way to achieve a desirable sample of grout. © 2017 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences
- Authors: Azadi, Mohammadreza , Taghichian, Ali , Taheri, Ali
- Date: 2017
- Type: Text , Journal article
- Relation: Journal of Rock Mechanics and Geotechnical Engineering Vol. 9, no. 4 (2017), p. 623-637
- Full Text:
- Reviewed:
- Description: Grout injection is used for sealing or strengthening the ground in order to prevent water entrance or any failure after excavation. There are many methods of grouting. Permeation grouting is one of the most common types in which the grout material is injected to the pore spaces of the ground. In grouting operations, the grout quality is important to achieve the best results. There are four main characteristics for a grout mixture including bleeding, setting time, strength, and viscosity. In this paper, we try to build some efficient grouting mixtures with different water to cement ratios considering these characteristics. The ingredients of grout mixtures built in this study are cement, water, bentonite, and some chemical additives such as sodium silicate, sodium carbonate, and triethanolamine (TEA). The grout mixtures are prepared for both of the sealing and strengthening purposes for a structural project. Effect of each above-mentioned ingredient is profoundly investigated. Since each ingredient may have positive or negative aspect, an optimization of appropriate amount of each ingredient is determined. The optimization is based on 200 grout mixture samples with different percentages of ingredients. Finally, some of these grout mixtures are chosen for the introduced project. It should be mentioned that grouting operations depend on various factors such as pressure of injection, ground structure and grain size of soils. However, quality of a grout can be helpful to make an injection easier and reasonable. For example, during the injection, a wrong estimated setting time can destroy the injected grout by washing the grout or setting early which prevents grouting. This paper tries to show some tests in easy way to achieve a desirable sample of grout. © 2017 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences
Prediction of blasting mean fragment size using support vector regression combined with five optimization algorithms
- Li, Enming, Yang, Fenghao, Ren, Meiheng, Zhang, Xiliang, Zhou, Jian, Khandelwal, Manoj
- Authors: Li, Enming , Yang, Fenghao , Ren, Meiheng , Zhang, Xiliang , Zhou, Jian , Khandelwal, Manoj
- Date: 2021
- Type: Text , Journal article
- Relation: Journal of Rock Mechanics and Geotechnical Engineering Vol. 13, no. 6 (2021), p. 1380-1397
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- Description: The main purpose of blasting operation is to produce desired and optimum mean size rock fragments. Smaller or fine fragments cause the loss of ore during loading and transportation, whereas large or coarser fragments need to be further processed, which enhances production cost. Therefore, accurate prediction of rock fragmentation is crucial in blasting operations. Mean fragment size (MFS) is a crucial index that measures the goodness of blasting designs. Over the past decades, various models have been proposed to evaluate and predict blasting fragmentation. Among these models, artificial intelligence (AI)-based models are becoming more popular due to their outstanding prediction results for multi-influential factors. In this study, support vector regression (SVR) techniques are adopted as the basic prediction tools, and five types of optimization algorithms, i.e. grid search (GS), grey wolf optimization (GWO), particle swarm optimization (PSO), genetic algorithm (GA) and salp swarm algorithm (SSA), are implemented to improve the prediction performance and optimize the hyper-parameters. The prediction model involves 19 influential factors that constitute a comprehensive blasting MFS evaluation system based on AI techniques. Among all the models, the GWO-v-SVR-based model shows the best comprehensive performance in predicting MFS in blasting operation. Three types of mathematical indices, i.e. mean square error (MSE), coefficient of determination (R2) and variance accounted for (VAF), are utilized for evaluating the performance of different prediction models. The R2, MSE and VAF values for the training set are 0.8355, 0.00138 and 80.98, respectively, whereas 0.8353, 0.00348 and 82.41, respectively for the testing set. Finally, sensitivity analysis is performed to understand the influence of input parameters on MFS. It shows that the most sensitive factor in blasting MFS is the uniaxial compressive strength. © 2021 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences
- Authors: Li, Enming , Yang, Fenghao , Ren, Meiheng , Zhang, Xiliang , Zhou, Jian , Khandelwal, Manoj
- Date: 2021
- Type: Text , Journal article
- Relation: Journal of Rock Mechanics and Geotechnical Engineering Vol. 13, no. 6 (2021), p. 1380-1397
- Full Text:
- Reviewed:
- Description: The main purpose of blasting operation is to produce desired and optimum mean size rock fragments. Smaller or fine fragments cause the loss of ore during loading and transportation, whereas large or coarser fragments need to be further processed, which enhances production cost. Therefore, accurate prediction of rock fragmentation is crucial in blasting operations. Mean fragment size (MFS) is a crucial index that measures the goodness of blasting designs. Over the past decades, various models have been proposed to evaluate and predict blasting fragmentation. Among these models, artificial intelligence (AI)-based models are becoming more popular due to their outstanding prediction results for multi-influential factors. In this study, support vector regression (SVR) techniques are adopted as the basic prediction tools, and five types of optimization algorithms, i.e. grid search (GS), grey wolf optimization (GWO), particle swarm optimization (PSO), genetic algorithm (GA) and salp swarm algorithm (SSA), are implemented to improve the prediction performance and optimize the hyper-parameters. The prediction model involves 19 influential factors that constitute a comprehensive blasting MFS evaluation system based on AI techniques. Among all the models, the GWO-v-SVR-based model shows the best comprehensive performance in predicting MFS in blasting operation. Three types of mathematical indices, i.e. mean square error (MSE), coefficient of determination (R2) and variance accounted for (VAF), are utilized for evaluating the performance of different prediction models. The R2, MSE and VAF values for the training set are 0.8355, 0.00138 and 80.98, respectively, whereas 0.8353, 0.00348 and 82.41, respectively for the testing set. Finally, sensitivity analysis is performed to understand the influence of input parameters on MFS. It shows that the most sensitive factor in blasting MFS is the uniaxial compressive strength. © 2021 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences
Intermittent swelling and shrinkage of a highly expansive soil treated with polyacrylamide
- Soltani, Amin, Deng, An, Taheri, Abbas, O'Kelly, Brendan
- Authors: Soltani, Amin , Deng, An , Taheri, Abbas , O'Kelly, Brendan
- Date: 2022
- Type: Text , Journal article
- Relation: Journal of Rock Mechanics and Geotechnical Engineering Vol. 14, no. 1 (2022), p. 252-261
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- Description: This laboratory study examines the potential use of an anionic polyacrylamide (PAM)-based material as an environmentally sustainable additive for the stabilization of an expansive soil from South Australia. The experimental program consisted of consistency limits, sediment volume, compaction and oedometer cyclic swell–shrink tests, performed using distilled water and four different PAM-to-water solutions of PD = 0.1 g/L, 0.2 g/L, 0.4 g/L and 0.6 g/L as the mixing liquids. Overall, the relative swelling and shrinkage strains were found to decrease with increasing number of applied swell–shrink cycles, with an ‘elastic equilibrium’ condition achieved on the conclusion of four cycles. The propensity for swelling/shrinkage potential reduction (for any given cycle) was found to be in favor of increasing the PAM dosage up to PD = 0.2 g/L, beyond which the excess PAM molecules self-associate as aggregates, thereby functioning as a lubricant instead of a flocculant; this critical dosage was termed ‘maximum flocculation dosage’ (MFD). The MFD assertion was discussed and validated using the consistency limits and sediment volume properties, both exhibiting only marginal variations beyond the identified MFD of PD = 0.2 g/L. The accumulated axial strain progressively transitioned from ‘expansive’ for the unamended soil to an ideal ‘neutral’ state at the MFD, while higher dosages demonstrated undesirable ‘contractive’ states. © 2022 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences
- Authors: Soltani, Amin , Deng, An , Taheri, Abbas , O'Kelly, Brendan
- Date: 2022
- Type: Text , Journal article
- Relation: Journal of Rock Mechanics and Geotechnical Engineering Vol. 14, no. 1 (2022), p. 252-261
- Full Text:
- Reviewed:
- Description: This laboratory study examines the potential use of an anionic polyacrylamide (PAM)-based material as an environmentally sustainable additive for the stabilization of an expansive soil from South Australia. The experimental program consisted of consistency limits, sediment volume, compaction and oedometer cyclic swell–shrink tests, performed using distilled water and four different PAM-to-water solutions of PD = 0.1 g/L, 0.2 g/L, 0.4 g/L and 0.6 g/L as the mixing liquids. Overall, the relative swelling and shrinkage strains were found to decrease with increasing number of applied swell–shrink cycles, with an ‘elastic equilibrium’ condition achieved on the conclusion of four cycles. The propensity for swelling/shrinkage potential reduction (for any given cycle) was found to be in favor of increasing the PAM dosage up to PD = 0.2 g/L, beyond which the excess PAM molecules self-associate as aggregates, thereby functioning as a lubricant instead of a flocculant; this critical dosage was termed ‘maximum flocculation dosage’ (MFD). The MFD assertion was discussed and validated using the consistency limits and sediment volume properties, both exhibiting only marginal variations beyond the identified MFD of PD = 0.2 g/L. The accumulated axial strain progressively transitioned from ‘expansive’ for the unamended soil to an ideal ‘neutral’ state at the MFD, while higher dosages demonstrated undesirable ‘contractive’ states. © 2022 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences
Understanding serendipity in science : a survey
- Yu, Shuo, Bedru, Hayat, Xinbei, Chu, Yuyuan, Yuan, Xia, Feng
- Authors: Yu, Shuo , Bedru, Hayat , Xinbei, Chu , Yuyuan, Yuan , Xia, Feng
- Date: 2021
- Type: Text , Journal article
- Relation: Data Analysis and Knowledge Discovery Vol. 5, no. 1 (2021), p. 16-35
- Full Text: false
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- Description: [Objective] This paper summarizes the components and definitions of serendipity, reviews representative supporting technologies and applications of serendipity in science, and discusses challenges and future directions in this field. [Coverage] We searched relevant keywords such as“serendipity”,“novelty”and “diversity”in research repositories such as Microsoft Academic and Google Scholar. A total of 102 well-selected references are finally cited. [Methods] We reviewed serendipitous discoveries in various scenarios, and discussed the concept of serendipity in the context of science. Relevant tools and applications are categorized. [Results] The tools that support serendipity are conducive to scientific research. However, there is no uniform definition of serendipity, thus making it difficult to measure serendipity in science. [Limitations] The factors affecting serendipity in science are complex, and yet to be explored. [Conclusions] Serendipity is one of the indispensable factors for scientific advances. However, many challenges are facing the exploration of serendipity in science, such as lack of metrics and difficulty to control. © 2021, Chinese Academy of Sciences. All rights reserved.
An evolutionary adaptive neuro-fuzzy inference system for estimating field penetration index of tunnel boring machine in rock mass
- Parsajoo, Maryama, Mohammed, Ahmed, Yagiz, Saffet, Armaghani, Danial, Khandelwal, Manoj
- Authors: Parsajoo, Maryama , Mohammed, Ahmed , Yagiz, Saffet , Armaghani, Danial , Khandelwal, Manoj
- Date: 2021
- Type: Text , Journal article
- Relation: Journal of Rock Mechanics and Geotechnical Engineering Vol. 13, no. 6 (2021), p. 1290-1299
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- Description: Field penetration index (FPI) is one of the representative key parameters to examine the tunnel boring machine (TBM) performance. Lack of accurate FPI prediction can be responsible for numerous disastrous incidents associated with rock mechanics and engineering. This study aims to predict TBM performance (i.e. FPI) by an efficient and improved adaptive neuro-fuzzy inference system (ANFIS) model. This was done using an evolutionary algorithm, i.e. artificial bee colony (ABC) algorithm mixed with the ANFIS model. The role of ABC algorithm in this system is to find the optimum membership functions (MFs) of ANFIS model to achieve a higher degree of accuracy. The procedure and modeling were conducted on a tunnelling database comprising of more than 150 data samples where brittleness index (BI), fracture spacing, α angle between the plane of weakness and the TBM driven direction, and field single cutter load were assigned as model inputs to approximate FPI values. According to the results obtained by performance indices, the proposed ANFIS_ABC model was able to receive the highest accuracy level in predicting FPI values compared with ANFIS model. In terms of coefficient of determination (R2), the values of 0.951 and 0.901 were obtained for training and testing stages of the proposed ANFIS_ABC model, respectively, which confirm its power and capability in solving TBM performance problem. The proposed model can be used in the other areas of rock mechanics and underground space technologies with similar conditions. © 2021 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences
- Authors: Parsajoo, Maryama , Mohammed, Ahmed , Yagiz, Saffet , Armaghani, Danial , Khandelwal, Manoj
- Date: 2021
- Type: Text , Journal article
- Relation: Journal of Rock Mechanics and Geotechnical Engineering Vol. 13, no. 6 (2021), p. 1290-1299
- Full Text:
- Reviewed:
- Description: Field penetration index (FPI) is one of the representative key parameters to examine the tunnel boring machine (TBM) performance. Lack of accurate FPI prediction can be responsible for numerous disastrous incidents associated with rock mechanics and engineering. This study aims to predict TBM performance (i.e. FPI) by an efficient and improved adaptive neuro-fuzzy inference system (ANFIS) model. This was done using an evolutionary algorithm, i.e. artificial bee colony (ABC) algorithm mixed with the ANFIS model. The role of ABC algorithm in this system is to find the optimum membership functions (MFs) of ANFIS model to achieve a higher degree of accuracy. The procedure and modeling were conducted on a tunnelling database comprising of more than 150 data samples where brittleness index (BI), fracture spacing, α angle between the plane of weakness and the TBM driven direction, and field single cutter load were assigned as model inputs to approximate FPI values. According to the results obtained by performance indices, the proposed ANFIS_ABC model was able to receive the highest accuracy level in predicting FPI values compared with ANFIS model. In terms of coefficient of determination (R2), the values of 0.951 and 0.901 were obtained for training and testing stages of the proposed ANFIS_ABC model, respectively, which confirm its power and capability in solving TBM performance problem. The proposed model can be used in the other areas of rock mechanics and underground space technologies with similar conditions. © 2021 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences
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