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The efficiency of tunnel boring machines (TBMs) in underground projects has great significance for the mining and tunneling industries, demanding a reliable estimation of the TBM’s performance in different geotechnical conditions. The current research work attempted to suggest an optimal predictor model of TBM performance as a reliable alternative to experimental and numerical techniques. To achieve this target, three data-mining techniques, namely neural network (NN), gene expression programming (GEP), and multivariate adaptive regression splines (MARS), were employed for modelling the TBM performance, and then the most robust predictive model was optimized via a metaheuristic search method known as whale optimization algorithm (WOA). For the modelling purpose, an experimental database was compiled by performing a field assessment program in a tunneling project in Malaysia and then conducting laboratory testing on the derived rock specimens. Based on the measured experimental data, the six most influential parameters were identified and served as model inputs to predict penetration rate (PR). In order to indicate the capability of the developed GEP and NN models, a stepwise linear regression model, i.e., MARS, was designed for PR prediction as well. The predictive capacity of the constructed models was quantified using a series of statistical indices, i.e., root mean squared error (RMSE), determination coefficient (R2) and variance account for (VAF). Based on the computed indices for testing records, both the proposed GEP and NN models (with RMSE values of 0.1882 and 0.2120 and R2 values of 0.9058 and 0.8735, respectively) yielded more accurate predictive results than the MARS model with RMSE of 0.2553 and R2 of 0.8346. Hence, by achieving the most robust performance compared to the rest, GEP-based model can provide a new practical equation with a high level of accuracy. In other part of this study, the six input parameters of the GEP model and its equation were, respectively, defined as decision variables and objective function for the WOA technique to find the optimum values of PR. As a consequence of optimizing the GEP equation, the maximum value of PR rose from 3.75 m/h to 4.022 m/h, equivalent to an increase of 7.25% in PR value. The findings of this study verified the applicability of the proposed hybrid GEP and WOA approach in the site investigation phase of tunneling projects constructed by TBMs.
Zimu Li; Behnam Yazdani Bejarbaneh; Panagiotis G. Asteris; Mohammadreza Koopialipoor; Danial Jahed Armaghani; M. M. Tahir. A hybrid GEP and WOA approach to estimate the optimal penetration rate of TBM in granitic rock mass. Soft Computing 2021, 25, 11877 -11895.
AMA StyleZimu Li, Behnam Yazdani Bejarbaneh, Panagiotis G. Asteris, Mohammadreza Koopialipoor, Danial Jahed Armaghani, M. M. Tahir. A hybrid GEP and WOA approach to estimate the optimal penetration rate of TBM in granitic rock mass. Soft Computing. 2021; 25 (17):11877-11895.
Chicago/Turabian StyleZimu Li; Behnam Yazdani Bejarbaneh; Panagiotis G. Asteris; Mohammadreza Koopialipoor; Danial Jahed Armaghani; M. M. Tahir. 2021. "A hybrid GEP and WOA approach to estimate the optimal penetration rate of TBM in granitic rock mass." Soft Computing 25, no. 17: 11877-11895.
The earth dam analysis under the strong seismic load like a destructive earthquake is one of the major topics with respect to the dynamical assessment. Damage control and the structural behaviour during an earthquake is a very important issue for an earthen dam. In this study, a comprehensive review is presented based on literature for dynamic analysis of earth dams. In this context, some significant factors are discussed such as plane stress, plane strain, data monitoring, application of finite-element method or finite-difference method, reinforcement, free vibration analysis, seismic cracks, liquefaction on dams, utilization of shaking table and centrifuge tests based on the small-scale physical modelling in order to validate any numerical analysis. To explain these parameters, case studies are discussed. It is observed that the earth dam structures had the integrated response to increasing the acceleration or displacement at the crest. Consequently, the interaction between the dam and reservoir also the foundation was a very effective factor to establish the nonlinear behaviour. It seems that the reinforced techniques are an essential approach to improve the structural response during an earthquake.
Behrouz Gordan; Mohammad Asif Raja; Danial Jahed Armaghani; Azlan Adnan. Review on Dynamic Behaviour of Earth Dam and Embankment During an Earthquake. Geotechnical and Geological Engineering 2021, 1 -31.
AMA StyleBehrouz Gordan, Mohammad Asif Raja, Danial Jahed Armaghani, Azlan Adnan. Review on Dynamic Behaviour of Earth Dam and Embankment During an Earthquake. Geotechnical and Geological Engineering. 2021; ():1-31.
Chicago/Turabian StyleBehrouz Gordan; Mohammad Asif Raja; Danial Jahed Armaghani; Azlan Adnan. 2021. "Review on Dynamic Behaviour of Earth Dam and Embankment During an Earthquake." Geotechnical and Geological Engineering , no. : 1-31.
Rock-burst is a common failure in hard rock related projects in civil and mining construction and therefore, proper classification and prediction of this phenomenon is of interest. This research presents the development of optimized naïve Bayes models, in predicting rock-burst failures in underground projects. The naïve Bayes models were optimized using four weight optimization techniques including forward, backward, particle swarm optimization, and evolutionary. An evolutionary random forest model was developed to identify the most significant input parameters. The maximum tangential stress, elastic energy index, and uniaxial tensile stress were then selected by the feature selection technique (i.e., evolutionary random forest) to develop the optimized naïve Bayes models. The performance of the models was assessed using various criteria as well as a simple ranking system. The results of this research showed that particle swarm optimization was the most effective technique in improving the accuracy of the naïve Bayes model for rock-burst prediction (cumulative ranking = 21), while the backward technique was the worst weight optimization technique (cumulative ranking = 11). All the optimized naïve Bayes models identified the maximum tangential stress as the most significant parameter in predicting rock-burst failures. The results of this research demonstrate that particle swarm optimization technique may improve the accuracy of naïve Bayes algorithms in predicting rock-burst occurrence.
Bo Ke; Manoj Khandelwal; Panagiotis G. Asteris; Athanasia D. Skentou; Anna Mamou; Danial Jahed Armaghani. Rock-Burst Occurrence Prediction Based on Optimized Naïve Bayes Models. IEEE Access 2021, 9, 91347 -91360.
AMA StyleBo Ke, Manoj Khandelwal, Panagiotis G. Asteris, Athanasia D. Skentou, Anna Mamou, Danial Jahed Armaghani. Rock-Burst Occurrence Prediction Based on Optimized Naïve Bayes Models. IEEE Access. 2021; 9 ():91347-91360.
Chicago/Turabian StyleBo Ke; Manoj Khandelwal; Panagiotis G. Asteris; Athanasia D. Skentou; Anna Mamou; Danial Jahed Armaghani. 2021. "Rock-Burst Occurrence Prediction Based on Optimized Naïve Bayes Models." IEEE Access 9, no. : 91347-91360.
This research examines the feasibility of hybridizing boosted Chi-Squared Automatic Interaction Detection (CHAID) with different kernels of support vector machine (SVM) techniques for the prediction of the peak particle velocity (PPV) induced by quarry blasting. To achieve this objective, a boosting-CHAID technique was applied to a big experimental database comprising six input variables. The technique identified four input parameters (distance from blast-face, stemming length, powder factor, and maximum charge per delay) as the most significant parameters affecting the prediction accuracy and utilized them to propose the SVM models with various kernels. The kernel types used in this study include radial basis function, polynomial, sigmoid, and linear. Several criteria, including mean absolute error (MAE), correlation coefficient (R), and gains, were calculated to evaluate the developed models’ accuracy and applicability. In addition, a simple ranking system was used to evaluate the models’ performance systematically. The performance of the R and MAE index of the radial basis function kernel of SVM in training and testing phases, respectively, confirm the high capability of this SVM kernel in predicting PPV values. This study successfully demonstrates that a combination of boosting-CHAID and SVM models can identify and predict with a high level of accuracy the most effective parameters affecting PPV values.
Jie Zeng; Panayiotis Roussis; Ahmed Mohammed; Chrysanthos Maraveas; Seyed Fatemi; Danial Armaghani; Panagiotis Asteris. Prediction of Peak Particle Velocity Caused by Blasting through the Combinations of Boosted-CHAID and SVM Models with Various Kernels. Applied Sciences 2021, 11, 3705 .
AMA StyleJie Zeng, Panayiotis Roussis, Ahmed Mohammed, Chrysanthos Maraveas, Seyed Fatemi, Danial Armaghani, Panagiotis Asteris. Prediction of Peak Particle Velocity Caused by Blasting through the Combinations of Boosted-CHAID and SVM Models with Various Kernels. Applied Sciences. 2021; 11 (8):3705.
Chicago/Turabian StyleJie Zeng; Panayiotis Roussis; Ahmed Mohammed; Chrysanthos Maraveas; Seyed Fatemi; Danial Armaghani; Panagiotis Asteris. 2021. "Prediction of Peak Particle Velocity Caused by Blasting through the Combinations of Boosted-CHAID and SVM Models with Various Kernels." Applied Sciences 11, no. 8: 3705.
A proper and reliable estimation of bearing capacity of thin-walled foundations is of importance and necessary for accurate design of these structures. This study proposes a new hybrid intelligent technique, i.e., adaptive neuro-fuzzy inference system (ANFIS)–polynomial neural network (PNN) optimized by the genetic algorithm (GA), called ANFIS–PNN–GA, for prediction of bearing capacity of the thin-walled foundations. In fact, in ANFIS–PNN–GA system, GA was used to optimize the ANFIS–PNN structure. To achieve the aim of this study, a series of data samples were collected from literature. After establishing the database, many ANFIS–PNN–GA models were constructed and proposed to estimate the bearing capacity of the aforementioned foundations. To show capability of this advance hybrid model, two pre-developed models i.e., ANFIS and PNN were also built to predict bearing capacity. The performance prediction of the proposed models were evaluated through the use of several performance indices, e.g., correlation coefficient (R) and mean square error (MSE). The R values of (0.9825, 0.9071, and 0.9928) and (0.8630, 0.7595 and 0.9241) were obtained for training and testing data of the ANFIS, PNN and ANFIS–PNN–GA, models, respectively. Accordingly, because of the role of GA as a practical optimization algorithm in improving the efficiency of both PNN and ANFIS models, results obtained by the ANFIS–PNN–GA model are more accurate compared to other implemented methods. The proposed advance hybrid model can be introduced as a new and applicable technique for solving problems in field of geotechnics and civil engineering.
Danial Jahed Armaghani; Hooman Harandizadeh; Ehsan Momeni. Load carrying capacity assessment of thin-walled foundations: an ANFIS–PNN model optimized by genetic algorithm. Engineering with Computers 2021, 1 -23.
AMA StyleDanial Jahed Armaghani, Hooman Harandizadeh, Ehsan Momeni. Load carrying capacity assessment of thin-walled foundations: an ANFIS–PNN model optimized by genetic algorithm. Engineering with Computers. 2021; ():1-23.
Chicago/Turabian StyleDanial Jahed Armaghani; Hooman Harandizadeh; Ehsan Momeni. 2021. "Load carrying capacity assessment of thin-walled foundations: an ANFIS–PNN model optimized by genetic algorithm." Engineering with Computers , no. : 1-23.
As a difficult and complex task, the accurate prediction of the tunnel boring machine (TBM) performance in various geological/ground conditions is of great importance and interest. Over the last decades, many rock mass classifications and field approaches have been developed to predict TBM performance in a reliable way. This study gives an overview of the mentioned models and their performance capacity in estimating TBM performance in different conditions. The review of rock mass classifications and field approaches indicated that these are considered as site-specific techniques and the performance prediction of these techniques is not satisfactory. In addition, these techniques are complex with many predictors or input parameters while providing all input parameters is sometimes impossible or very difficult for a specific tunnelling project. This research suggests other techniques such as statistical-based and computational-based in order to get a higher level of accuracy in the area of TBM performance.
Danial Jahed Armaghani; Aydin Azizi. An Overview of Field Classifications to Evaluate Tunnel Boring Machine Performance. Tunable Low-Power Low-Noise Amplifier for Healthcare Applications 2021, 1 -16.
AMA StyleDanial Jahed Armaghani, Aydin Azizi. An Overview of Field Classifications to Evaluate Tunnel Boring Machine Performance. Tunable Low-Power Low-Noise Amplifier for Healthcare Applications. 2021; ():1-16.
Chicago/Turabian StyleDanial Jahed Armaghani; Aydin Azizi. 2021. "An Overview of Field Classifications to Evaluate Tunnel Boring Machine Performance." Tunable Low-Power Low-Noise Amplifier for Healthcare Applications , no. : 1-16.
This study aims to propose a practical intelligence way for the prediction of tunnel boring machine (TBM) performance in various weathering zones. To do this, after reviewing the available literature, the data collected from the tunnel site and doing laboratory investigations, five important parameters, i.e., rock mass rating, Brazilian tensile strength, weathering zone, cutter head thrust force, and revolution per minute, were set as model inputs to predict penetration rate (PR) of TBM. Then, two intelligence techniques, namely, group method of data handling (GMDH) and artificial neural network (ANN) were applied to the collected data (i.e., 202 data samples). In developing these intelligence techniques, a series of parametric studies were conducted on the most important parameters of these techniques. After developing GMDH and ANN models, some important performance indices were selected and calculated to select the best one among them. It was found that the GMDH model receives a higher accuracy level compared to the ANN model. It can be established that the GMDH is an applicable and powerful technique in the area of TBM and tunnelling technology.
Danial Jahed Armaghani; Aydin Azizi. A Comparative Study of Artificial Intelligence Techniques to Estimate TBM Performance in Various Weathering Zones. Tunable Low-Power Low-Noise Amplifier for Healthcare Applications 2021, 55 -70.
AMA StyleDanial Jahed Armaghani, Aydin Azizi. A Comparative Study of Artificial Intelligence Techniques to Estimate TBM Performance in Various Weathering Zones. Tunable Low-Power Low-Noise Amplifier for Healthcare Applications. 2021; ():55-70.
Chicago/Turabian StyleDanial Jahed Armaghani; Aydin Azizi. 2021. "A Comparative Study of Artificial Intelligence Techniques to Estimate TBM Performance in Various Weathering Zones." Tunable Low-Power Low-Noise Amplifier for Healthcare Applications , no. : 55-70.
The efficiency of tunnel boring machines (TBMs) in tunnelling projects has great importance for civil and geotechnical industries. A reliable and applicable model for predicting TBM performance is of interest and necessity in any tunnelling project before construction and even ordering TBM machine. In this study, a series of statistical-based models/equations, i.e., simple regression, linear, and non-linear multiple regression (LMR and NLMR) models were developed to predict TBM performance including advance rate, AR, and penetration rate, PR. The most effective parameters on TBM performance based on different categories of rock material, rock mass, and machine properties were selected and used. Results obtained by simple regression models showed that they are not good enough for receiving a suitable accuracy in predicting TBM PR/AR. In addition, LMR and NLMR equations received a higher performance prediction compared to simple regression models. A coefficient of determination of about 0.6 confirmed a suitable and applicable accuracy level for the developed LMR and NLMR equations in estimating TBM PR/AR.
Danial Jahed Armaghani; Aydin Azizi. Developing Statistical Models for Solving Tunnel Boring Machine Performance Problem. Tunable Low-Power Low-Noise Amplifier for Healthcare Applications 2021, 33 -53.
AMA StyleDanial Jahed Armaghani, Aydin Azizi. Developing Statistical Models for Solving Tunnel Boring Machine Performance Problem. Tunable Low-Power Low-Noise Amplifier for Healthcare Applications. 2021; ():33-53.
Chicago/Turabian StyleDanial Jahed Armaghani; Aydin Azizi. 2021. "Developing Statistical Models for Solving Tunnel Boring Machine Performance Problem." Tunable Low-Power Low-Noise Amplifier for Healthcare Applications , no. : 33-53.
The use of tunnel boring machine (TBM) in mechanized tunnelling excavation in various ground conditions has been highlighted in many projects. In these projects, estimation of the TBM performance is considered as a significant issue since it can be an influential parameter related to the project cost. Hence, many scholars tried to develop simple, applicable, and powerful methodologies for the prediction of TBM performance. The total developed methods in this regard can be divided into four categories, namely, theoretical, empirical, statistical, and computational. In this study, the advantages and disadvantages of these techniques were discussed. Many investigators mentioned that empirical and theoretical techniques are not good enough in accurate prediction of TBM performance. Some other researchers developed statistical-based models/equations in predicting TBM performance. However, their accuracy level is only suitable (coefficient of determination ~0.6) in many cases. On the other hand, these techniques are not good if there are some outlier data samples in the database. The best model category for TBM performance prediction is related to machine learning (ML) and artificial intelligence (AI) techniques. Using these techniques, a complex problem (i.e., TBM performance) can be solved with a high level of accuracy and low level of system error (coefficient of determination ~0.9). This study concluded that ML and AI are considered as accurate, powerful, and simple techniques in the area of tunnelling and they can be used in other applications of geotechnics as well.
Danial Jahed Armaghani; Aydin Azizi. Empirical, Statistical, and Intelligent Techniques for TBM Performance Prediction. Tunable Low-Power Low-Noise Amplifier for Healthcare Applications 2021, 17 -32.
AMA StyleDanial Jahed Armaghani, Aydin Azizi. Empirical, Statistical, and Intelligent Techniques for TBM Performance Prediction. Tunable Low-Power Low-Noise Amplifier for Healthcare Applications. 2021; ():17-32.
Chicago/Turabian StyleDanial Jahed Armaghani; Aydin Azizi. 2021. "Empirical, Statistical, and Intelligent Techniques for TBM Performance Prediction." Tunable Low-Power Low-Noise Amplifier for Healthcare Applications , no. : 17-32.
Thermal conductivity is a specific thermal property of soil which controls the exchange of thermal energy. If predicted accurately, the thermal conductivity of soil has a significant effect on geothermal applications. Since the thermal conductivity is influenced by multiple variables including soil type and mineralogy, dry density, and water content, its precise prediction becomes a challenging problem. In this study, novel computational approaches including hybridisation of two metaheuristic optimisation algorithms, i.e. firefly algorithm (FF) and improved firefly algorithm (IFF), with conventional machine learning techniques including extreme learning machine (ELM), adaptive neuro-fuzzy interface system (ANFIS) and artificial neural network (ANN), are proposed to predict the thermal conductivity of unsaturated soils. FF and IFF are used to optimise the internal parameters of the ELM, ANFIS and ANN. These six hybrid models are applied to the dataset of 257 soil cases considering six influential variables for predicting the thermal conductivity of unsaturated soils. Several performance parameters are used to verify the predictive performance and generalisation capability of the developed hybrid models. The obtained results from the computational process confirmed that ELM-IFF attained the best predictive performance with a coefficient of determination = 0.9615, variance account for = 96.06%, root mean square error = 0.0428, and mean absolute error = 0.0316 on the testing dataset (validation phase). The results of the models are also visualised and analysed through different approaches using Taylor diagrams, regression error characteristic curves and area under curve scores, rank analysis and a novel method called accuracy matrix. It was found that all the proposed hybrid models have a great ability to be considered as alternatives for empirical relevant models. The developed ELM-IFF model can be employed in the initial stages of any engineering projects for fast determination of thermal conductivity.
Navid Kardani; Abidhan Bardhan; Pijush Samui; Majidreza Nazem; Annan Zhou; Danial Jahed Armaghani. A novel technique based on the improved firefly algorithm coupled with extreme learning machine (ELM-IFF) for predicting the thermal conductivity of soil. Engineering with Computers 2021, 1 -20.
AMA StyleNavid Kardani, Abidhan Bardhan, Pijush Samui, Majidreza Nazem, Annan Zhou, Danial Jahed Armaghani. A novel technique based on the improved firefly algorithm coupled with extreme learning machine (ELM-IFF) for predicting the thermal conductivity of soil. Engineering with Computers. 2021; ():1-20.
Chicago/Turabian StyleNavid Kardani; Abidhan Bardhan; Pijush Samui; Majidreza Nazem; Annan Zhou; Danial Jahed Armaghani. 2021. "A novel technique based on the improved firefly algorithm coupled with extreme learning machine (ELM-IFF) for predicting the thermal conductivity of soil." Engineering with Computers , no. : 1-20.
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.
Canxin Yu; Mohammadreza Koopialipoor; Bhatawdekar Ramesh Murlidhar; Ahmed Salih Mohammed; Danial Jahed Armaghani; Edy Tonnizam Mohamad; Zengli Wang. Optimal ELM–Harris Hawks Optimization and ELM–Grasshopper Optimization Models to Forecast Peak Particle Velocity Resulting from Mine Blasting. Natural Resources Research 2021, 30, 2647 -2662.
AMA StyleCanxin Yu, Mohammadreza Koopialipoor, Bhatawdekar Ramesh Murlidhar, Ahmed Salih Mohammed, Danial Jahed Armaghani, Edy Tonnizam Mohamad, Zengli Wang. Optimal ELM–Harris Hawks Optimization and ELM–Grasshopper Optimization Models to Forecast Peak Particle Velocity Resulting from Mine Blasting. Natural Resources Research. 2021; 30 (3):2647-2662.
Chicago/Turabian StyleCanxin Yu; Mohammadreza Koopialipoor; Bhatawdekar Ramesh Murlidhar; Ahmed Salih Mohammed; Danial Jahed Armaghani; Edy Tonnizam Mohamad; Zengli Wang. 2021. "Optimal ELM–Harris Hawks Optimization and ELM–Grasshopper Optimization Models to Forecast Peak Particle Velocity Resulting from Mine Blasting." Natural Resources Research 30, no. 3: 2647-2662.
Buried pipes are extensively used for oil transportation from offshore platforms. Under unfavorable loading combinations, the pipe’s uplift resistance may be exceeded, which may result in excessive deformations and significant disruptions. This paper presents findings from a series of small-scale tests performed on pipes buried in geogrid-reinforced sands, with the measured peak uplift resistance being used to calibrate advanced numerical models employing neural networks. Multilayer perceptron (MLP) and Radial Basis Function (RBF) primary structure types have been used to train two neural network models, which were then further developed using bagging and boosting ensemble techniques. Correlation coefficients in excess of 0.954 between the measured and predicted peak uplift resistance have been achieved. The results show that the design of pipelines can be significantly improved using the proposed novel, reliable and robust soft computing models.
Jie Zeng; Panagiotis G. Asteris; Anna P. Mamou; Ahmed Salih Mohammed; Emmanuil A. Golias; Danial Jahed Armaghani; Koohyar Faizi; Mahdi Hasanipanah. The Effectiveness of Ensemble-Neural Network Techniques to Predict Peak Uplift Resistance of Buried Pipes in Reinforced Sand. Applied Sciences 2021, 11, 908 .
AMA StyleJie Zeng, Panagiotis G. Asteris, Anna P. Mamou, Ahmed Salih Mohammed, Emmanuil A. Golias, Danial Jahed Armaghani, Koohyar Faizi, Mahdi Hasanipanah. The Effectiveness of Ensemble-Neural Network Techniques to Predict Peak Uplift Resistance of Buried Pipes in Reinforced Sand. Applied Sciences. 2021; 11 (3):908.
Chicago/Turabian StyleJie Zeng; Panagiotis G. Asteris; Anna P. Mamou; Ahmed Salih Mohammed; Emmanuil A. Golias; Danial Jahed Armaghani; Koohyar Faizi; Mahdi Hasanipanah. 2021. "The Effectiveness of Ensemble-Neural Network Techniques to Predict Peak Uplift Resistance of Buried Pipes in Reinforced Sand." Applied Sciences 11, no. 3: 908.
A proper planning schedule for tunnel boring machine (TBM) construction is considered as a necessary and difficult task in tunneling projects. Therefore, prediction of TBM performance with high degree of accuracy is needed to prepare a suitable planning schedule. This study aims to predict the advance rate of TBMs using optimized extreme learning machine (ELM) model with six particles swam optimization (PSO) techniques. Hence, six deterministically adaptive models, including time-varying acceleration (TAC)–PSO–ELM, improved PSO–ELM, Modified PSO–ELM, TAC–MeanPSO–ELM, improved MeanPSO–ELM, and Modified MeanPSO–ELM were developed. A number of performance criteria along with ranking system were used to identify the best model. The results showed that modified MeanPSO–ELM achieved the highest cumulative ranking (56), while the modified PSO–ELM achieved the lowest cumulative ranking (51). For training phase, improved PSO–ELM and TAC–PSO–ELM achieved the highest ranking (30) for each. The TAC–MeanPSO–ELM obtained the lowest ranking in the testing phase (29). Concerning the coefficient of determination (R2), modified PSO–ELM, improved PSO–ELM, TAC–PSO–ELM, and modified MeanPSO–ELM showed a similar behavior and achieved 0.97 for training and 0.96 for testing phases. Two models, including improved MeanPSO–ELM and TAC–MeanPSO–ELM achieved the same R2 of 0.96 for both training and testing phases. The findings of this study suggest that the hybridization of ELM and PSO may result in more accurate results than single ELM model to predict the TBM advance rate.
Jie Zeng; Bishwajit Roy; Deepak Kumar; Ahmed Salih Mohammed; Danial Jahed Armaghani; Jian Zhou; Edy Tonnizam Mohamad. Proposing several hybrid PSO-extreme learning machine techniques to predict TBM performance. Engineering with Computers 2021, 1 -17.
AMA StyleJie Zeng, Bishwajit Roy, Deepak Kumar, Ahmed Salih Mohammed, Danial Jahed Armaghani, Jian Zhou, Edy Tonnizam Mohamad. Proposing several hybrid PSO-extreme learning machine techniques to predict TBM performance. Engineering with Computers. 2021; ():1-17.
Chicago/Turabian StyleJie Zeng; Bishwajit Roy; Deepak Kumar; Ahmed Salih Mohammed; Danial Jahed Armaghani; Jian Zhou; Edy Tonnizam Mohamad. 2021. "Proposing several hybrid PSO-extreme learning machine techniques to predict TBM performance." Engineering with Computers , no. : 1-17.
The identification of parameters that affect mining is one of the requirements in executive work in this field. Due to the dangers of flyrock, studying the role of the factors that affect it will be useful to control this serious environmental issue of blasting. In this research, using hybrid intelligence techniques, a new guide to investigate the parameters that affect the occurrence and characteristics of flyrock is presented. Hybrid models were improved based on five types of optimization algorithms, namely particle swarm optimization, artificial bee colony, the imperialist competitive algorithm, firefly algorithm (FA), and genetic algorithm. The process of designing the structure of the models was controlled under the fuzzy Delphi method. This filter helps to determine the most important factors that play a key role in the flyrock phenomenon and its accurate prediction. The best optimization technique was selected based on applying two popular performance indices, i.e., the root-mean-square error and coefficient of determination (R2). As a result, the best combination obtained was the FA-artificial neural network (ANN), which was able to provide the best optimization of the weights and biases of the ANN among all the proposed models. In addition, this system showed the lowest network error in the prediction of flyrock compared to other ANN-based models. The new combination (FA-ANN) can be used as a powerful and practical technique to predict the flyrock distance prior to blasting operations.
Diyuan Li; Mohammadreza Koopialipoor; Danial Jahed Armaghani. A Combination of Fuzzy Delphi Method and ANN-based Models to Investigate Factors of Flyrock Induced by Mine Blasting. Natural Resources Research 2021, 30, 1905 -1924.
AMA StyleDiyuan Li, Mohammadreza Koopialipoor, Danial Jahed Armaghani. A Combination of Fuzzy Delphi Method and ANN-based Models to Investigate Factors of Flyrock Induced by Mine Blasting. Natural Resources Research. 2021; 30 (2):1905-1924.
Chicago/Turabian StyleDiyuan Li; Mohammadreza Koopialipoor; Danial Jahed Armaghani. 2021. "A Combination of Fuzzy Delphi Method and ANN-based Models to Investigate Factors of Flyrock Induced by Mine Blasting." Natural Resources Research 30, no. 2: 1905-1924.
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.
Binh Thai Pham; Manh Duc Nguyen; Trung Nguyen-Thoi; Lanh Si Ho; Mohammadreza Koopialipoor; Nguyen Kim Quoc; Danial Jahed Armaghani; Hiep Van Le. A novel approach for classification of soils based on laboratory tests using Adaboost, Tree and ANN modeling. Transportation Geotechnics 2020, 27, 100508 .
AMA StyleBinh Thai Pham, Manh Duc Nguyen, Trung Nguyen-Thoi, Lanh Si Ho, Mohammadreza Koopialipoor, Nguyen Kim Quoc, Danial Jahed Armaghani, Hiep Van Le. A novel approach for classification of soils based on laboratory tests using Adaboost, Tree and ANN modeling. Transportation Geotechnics. 2020; 27 ():100508.
Chicago/Turabian StyleBinh Thai Pham; Manh Duc Nguyen; Trung Nguyen-Thoi; Lanh Si Ho; Mohammadreza Koopialipoor; Nguyen Kim Quoc; Danial Jahed Armaghani; Hiep Van Le. 2020. "A novel approach for classification of soils based on laboratory tests using Adaboost, Tree and ANN modeling." Transportation Geotechnics 27, no. : 100508.
Blasting operations typically have several negative impacts upon human beings and constructions in adjacent region. Among all, air-overpressure (AOp) has been persistently attractive to practitioners and researchers. To control the AOp-induced damage, its strength should be predicted before conducting a blasting operation. This paper analyzes the AOp consequences through the use of the Fuzzy Delphi method (FDM). The method was adopted to identify the key variables with the deepest influence on AOp based on the experts’ opinions. Then, the most effective parameters on AOp were selected to be used in developing a new hybrid intelligent technique, i.e., adaptive neuro-fuzzy inference system (ANFIS)-polynomial neural network (PNN) optimized by the genetic algorithm (GA), called ANFIS-PNN-GA. From FDM and experts’ opinions, four parameters, i.e., amount of explosive charge, powder factor, stemming, and distance from the blast-face were identified as the most effective ones on AOp. In fact, in ANFIS-PNN-GA system, GA was used to optimize the ANFIS-PNN structure. The new framework of ANFIS-PNN-GA was developed, trained, and tested on actual datasets collected from a total of 62 blasting events. To show capability of the newly-proposed model, the ANFIS and PNN predictive models were also constructed to estimate AOp, and the performance prediction of the proposed models were evaluated through the use of several performance indices, e.g., correlation coefficient (R) and mean square error (MSE). R values of (0.94, 0.72, and 0.84) and (0.92, 0.58, and 0.77) and MSE values of (0.003, 0.03, and 0.021) and (0.005, 0.066, and 0.05) were obtained for training and testing datasets of ANFIS-PNN-GA, PNN, and ANFIS models, respectively. Accordingly, because of the role of GA as a practical optimization algorithm in improving the efficiency of both PNN and ANFIS models, results obtained by the ANFIS-PNN-GA model are more accurate compared to other implemented methods.
Hooman Harandizadeh; Danial Jahed Armaghani. Prediction of air-overpressure induced by blasting using an ANFIS-PNN model optimized by GA. Applied Soft Computing 2020, 99, 106904 .
AMA StyleHooman Harandizadeh, Danial Jahed Armaghani. Prediction of air-overpressure induced by blasting using an ANFIS-PNN model optimized by GA. Applied Soft Computing. 2020; 99 ():106904.
Chicago/Turabian StyleHooman Harandizadeh; Danial Jahed Armaghani. 2020. "Prediction of air-overpressure induced by blasting using an ANFIS-PNN model optimized by GA." Applied Soft Computing 99, no. : 106904.
This study combined a fuzzy Delphi method (FDM) and two advanced decision-tree algorithms to predict air-overpressure (AOp) caused by mine blasting. The FDM was used for input selection. Thus, the panel of experts selected four inputs, including powder factor, max charge per delay, stemming length, and distance from the blast face. Once the input selection was completed, two decision-tree algorithms, namely extreme gradient boosting tree (XGBoost-tree) and random forest (RF), were applied using the inputs selected by the experts. The models are evaluated with the following criteria: correlation coefficient, mean absolute error, gains chart, and Taylor diagram. The applied models were compared with the XGBoost-tree and RF models using the full set of data without input selection results. The results of hybridization showed that the XGBoost-tree model outperformed the RF model. Concerning the gains, the XGBoost-tree again outperformed the RF model. In comparison with the single decision-tree models, the single models had slightly better correlation coefficients; however, the hybridized models were simpler and easier to understand, analyze and implement. In addition, the Taylor diagram showed that the models applied outperformed some other conventional machine learning models, including support vector machine, k-nearest neighbors, and artificial neural network. Overall, the findings of this study suggest that combining expert opinion and advanced decision-tree algorithms can result in accurate and easy to understand predictions of AOp resulting from blasting in quarry sites.
Ziguang He; Danial Jahed Armaghani; Mojtaba Masoumnezhad; Manoj Khandelwal; Jian Zhou; Bhatawdekar Ramesh Murlidhar. A Combination of Expert-Based System and Advanced Decision-Tree Algorithms to Predict Air-Overpressure Resulting from Quarry Blasting. Natural Resources Research 2020, 30, 1889 -1903.
AMA StyleZiguang He, Danial Jahed Armaghani, Mojtaba Masoumnezhad, Manoj Khandelwal, Jian Zhou, Bhatawdekar Ramesh Murlidhar. A Combination of Expert-Based System and Advanced Decision-Tree Algorithms to Predict Air-Overpressure Resulting from Quarry Blasting. Natural Resources Research. 2020; 30 (2):1889-1903.
Chicago/Turabian StyleZiguang He; Danial Jahed Armaghani; Mojtaba Masoumnezhad; Manoj Khandelwal; Jian Zhou; Bhatawdekar Ramesh Murlidhar. 2020. "A Combination of Expert-Based System and Advanced Decision-Tree Algorithms to Predict Air-Overpressure Resulting from Quarry Blasting." Natural Resources Research 30, no. 2: 1889-1903.
This study presents a new input parameter selection and modeling procedure in order to control and predict peak particle velocity (PPV) values induced by mine blasting. The first part of this study was performed through the use of fuzzy Delphi method (FDM) to identify the key input variables with the deepest influence on PPV based on the experts’ opinions. Then, in the second part, the most effective parameters on PPV were selected to be applied in hybrid artificial neural network (ANN)-based models i.e., genetic algorithm (GA)-ANN, particle swarm optimization (PSO)-ANN, imperialism competitive algorithm (ICA)-ANN, artificial bee colony (ABC)-ANN and firefly algorithm (FA)-ANN for the prediction of PPV. Many hybrid ANN-based models were constructed according to the most influential parameters of GA, PSO, ICA, ABC and FA optimization techniques and 5 hybrid ANN-based models were proposed to predict PPVs induced by blasting. Through simple ranking technique, the best hybrid model was selected. The obtained results revealed that the FA-ANN model is able to offer higher accuracy level for PPV prediction compared to other implemented hybrid models. Coefficient of determination (R2) results of (0.8831, 0.8995, 0.9043, 0.9095 and 0.9133) and (0.8657, 0.8749, 0.8850, 0.9094 and 0.9097) were obtained for train and test stages of GA-ANN, PSO-ANN, ICA-ANN, ABC-ANN and FA-ANN, respectively. The results showed that all hybrid models can be used to solve PPV problem, however, when the highest prediction performance is needed, the hybrid FA-ANN model would be the best choice.
Jiandong Huang; Mohammadreza Koopialipoor; Danial Jahed Armaghani. A combination of fuzzy Delphi method and hybrid ANN-based systems to forecast ground vibration resulting from blasting. Scientific Reports 2020, 10, 1 -21.
AMA StyleJiandong Huang, Mohammadreza Koopialipoor, Danial Jahed Armaghani. A combination of fuzzy Delphi method and hybrid ANN-based systems to forecast ground vibration resulting from blasting. Scientific Reports. 2020; 10 (1):1-21.
Chicago/Turabian StyleJiandong Huang; Mohammadreza Koopialipoor; Danial Jahed Armaghani. 2020. "A combination of fuzzy Delphi method and hybrid ANN-based systems to forecast ground vibration resulting from blasting." Scientific Reports 10, no. 1: 1-21.
The advance rate (AR) of a tunnel boring machine (TBM) in hard rock condition is a key parameter for the successful accomplishment of a tunneling project, and the proper and reliable prediction of this parameter can lead to minimizing the risks associated to high capital costs and scheduling for such projects. This research aims at optimizing the hyper-parameters of the support vector machine (SVM) technique through the use of three optimization algorithms, namely, gray wolf optimization (GWO), whale optimization algorithm (WOA) and moth flame optimization (MFO), in forecasting TBM AR. In fact, the role of these optimization techniques is to optimize the hyperparameters ‘C’ and ‘gamma’ of the SVM model to get higher performance prediction. To develop the hybrid SVM-based models, 1,286 sample sets of data collected from a water transfer tunnel in Malaysia comprising seven input variables, i.e., rock mass rating, uniaxial compressive strength, Brazilian tensile strength, rock quality designation, weathering zone, thrust force and revolution per minute, and one output variable, i.e., TBM AR, were considered and used. Several GWO-SVM, WOA-SVM and MFO-SVM models were constructed to predict TBM AR considering their effective parameters. The accuracy levels of the proposed models were assessed using four statistical indices, i.e., the coefficient of determination (R2), root mean squared error (RMSE), mean absolute error (MAE), and variance accounted for (VAF). Modeling results revealed that the MFO algorithm can capture better hyper-parameters of the SVM model in predicting TBM AR among all three hybrid models. R2 of (0.9623 and 0.9724), RMSE of (0.1269 and 0.1155), and VAF of (96.24 and 97.34%), respectively, for training and test stages of the MFO-SVM model confirmed that this hybrid SVM model is a powerful and applicable technique addressing problems related to TBM performance with a high level of accuracy.
Jian Zhou; Yingui Qiu; Shuangli Zhu; Danial Jahed Armaghani; Chuanqi Li; Hoang Nguyen; Saffet Yagiz. Optimization of support vector machine through the use of metaheuristic algorithms in forecasting TBM advance rate. Engineering Applications of Artificial Intelligence 2020, 97, 104015 .
AMA StyleJian Zhou, Yingui Qiu, Shuangli Zhu, Danial Jahed Armaghani, Chuanqi Li, Hoang Nguyen, Saffet Yagiz. Optimization of support vector machine through the use of metaheuristic algorithms in forecasting TBM advance rate. Engineering Applications of Artificial Intelligence. 2020; 97 ():104015.
Chicago/Turabian StyleJian Zhou; Yingui Qiu; Shuangli Zhu; Danial Jahed Armaghani; Chuanqi Li; Hoang Nguyen; Saffet Yagiz. 2020. "Optimization of support vector machine through the use of metaheuristic algorithms in forecasting TBM advance rate." Engineering Applications of Artificial Intelligence 97, no. : 104015.
In surface mines and underground excavations, every blasting operation can have some destructive environmental impacts, among which air overpressure (AOp) is of major significance. Therefore, it is essential to minimize the related environmental damage by precisely evaluating the intensity of AOp before any blasting operation. The present study primarily aimed to develop two different tree-based data mining algorithms, namely M5′ decision tree and genetic programming (GP) for accurately predicting blast-induced AOp in granite quarries. In addition, a multiple linear regression technique was adopted to check the accuracy of the GP and M5′ models. To achieve the aims of this research, 125 blasts were explored and their respective AOp values were carefully recorded. In each operation, six influential parameters of AOp, i.e., stemming length, powder factor, blasting index, joint aperture, maximum charge weight per delay and distance of the blast points, were recorded and considered as inputs for modeling. After developing the predictive models of AOp, their performances were examined in terms of coefficient of determination (R2), root-mean-squared error (RMSE) and mean absolute error (MAE). Based on the computed results, the GP (with RMSE of 1.3997, R2 of 0.8621 and MAE of 0.9472) outperformed the other developed models. Then, a sensitivity analysis was employed to identify the most influential parameters in predicting the AOp values. Finally, the generality of the proposed GP model was validated by investigating its predictive results with respect to the two most effective predictor variables. The study findings demonstrated the robustness and applicability of the proposed GP model for predicting blast-induced AOp.
Bhatawdekar Ramesh Murlidhar; Behnam Yazdani Bejarbaneh; Danial Jahed Armaghani; Ahmed Salih Mohammed; Edy Tonnizam Mohamad. Application of Tree-Based Predictive Models to Forecast Air Overpressure Induced by Mine Blasting. Natural Resources Research 2020, 30, 1865 -1887.
AMA StyleBhatawdekar Ramesh Murlidhar, Behnam Yazdani Bejarbaneh, Danial Jahed Armaghani, Ahmed Salih Mohammed, Edy Tonnizam Mohamad. Application of Tree-Based Predictive Models to Forecast Air Overpressure Induced by Mine Blasting. Natural Resources Research. 2020; 30 (2):1865-1887.
Chicago/Turabian StyleBhatawdekar Ramesh Murlidhar; Behnam Yazdani Bejarbaneh; Danial Jahed Armaghani; Ahmed Salih Mohammed; Edy Tonnizam Mohamad. 2020. "Application of Tree-Based Predictive Models to Forecast Air Overpressure Induced by Mine Blasting." Natural Resources Research 30, no. 2: 1865-1887.