Feasibility of the indirect determination of blast-induced rock movement based on three new hybrid intelligent models

Yu, Zhi; Shi, Xiuzhi; Zhou, Jian; Rao, D. Ganeshwar; Chen, Xin; Wang, Dong; Miao, Xiaohu; Ipangelwa, Timo

doi:10.1007/s00366-019-00868-0

Cited by 39 publications

(11 citation statements)

References 54 publications

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“…ese algorithms are used to optimize the performance of machine learning model to achieve a balance between model accuracy and model generalization. e employed metaheuristic approaches include symbiotic organisms search [42], particle swarm optimization [43,44], the forensic-based investigation optimization [45], equilibrium optimization [20], Harris hawks optimization [46], simulated annealing [47], social spider optimization [48,49], gray wolf optimization [38,50], teaching-learningbased algorithm [51], salp swarm algorithm [52,53], artificial bee colony [54], pigeon-inspired optimization [55], cuckoo search optimization [56], imperialist competitive algorithm [57], moth flame optimization [58], and cuckoo search algorithm [59]. ose previous works have demonstrated the effectiveness of metaheuristic algorithms in optimizing machine learning models and solving complex tasks in various application domains.…”

Section: Research Background and Motivationmentioning

confidence: 99%

Remote Sensing–Based Urban Green Space Detection Using Marine Predators Algorithm Optimized Machine Learning Approach

Hoang

Tran

2021

Mathematical Problems in Engineering

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Information regarding the current status of urban green space is crucial for urban land-use planning and management. This study proposes a remote sensing and data-driven solution for urban green space detection at regional scale via employment of state-of-the-art metaheuristic and machine learning approaches. Remotely sensed data obtained from Sentinel 2 satellite in the study area of Da Nang city (Vietnam) are used to construct and verify an intelligent model that hybridizes Marine Predators Algorithm (MPA) and support vector machines (SVM). SVM are employed to generalize a decision boundary that separates features characterizing statistical measurements of remote sensing data into two categories of “green space” and “nongreen space”. The MPA metaheuristic is used to optimize the SVM training phase by identifying an appropriate set of the SVM’s hyperparameters including the penalty coefficient and the kernel function parameter. Experimental results show that the proposed model which processes information provided by all of the Sentinel 2 satellite’s spectral bands can deliver a better performance than those obtained from the model based on vegetation indices. With a good classification accuracy rate of roughly 93%, an F1 score = 0.93, and an area under the receiver operating characteristic = 0.98, the newly developed model is a promising tool to assist local authority to obtain up-to-date information on urban green space and develop plans of sustainable urban land use.

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Section: Research Background and Motivationmentioning

confidence: 99%

Remote Sensing–Based Urban Green Space Detection Using Marine Predators Algorithm Optimized Machine Learning Approach

Hoang

Tran

2021

Mathematical Problems in Engineering

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“…By taking Python 2.7 and MATLAB 2014 as the calculation platform, the measured values of level 3 risk indicators and risk category labels of level 2 risk indicators of 30 monitoring points in the Wangzong section of Wuhan Metro Line 3 are taken as the training data of the model. Second, the three models are trained separately, and the optimal number of parameters and the indicator of mode error for each model input are shown in Table 3 (Yu et al, 2021). Finally, RMSE as given by Eqn (6) and R 2 as given by Eqn (7) in Section 3.4 are used to measure the prediction accuracy of the model.…”

Section: Algorithm Error Analysismentioning

confidence: 99%

Risk Prediction and Diagnosis of Water Seepage in Operational Shield Tunnels Based on Random Forest

Liu

Chen

Zhang

et al. 2021

Journal of Civil Engineering and Management

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Water seepage (WS) is a paramount defect during tunnel operation and directly affects the operational safety of tunnels. Effectively predicting and diagnosing WS are problems that urgently need to be solved. This paper presents a standard and an evaluation index system for WS grades and constructs a sample dataset from monitoring recoreds for demonstration purposes. First, we use bootstrap resampling to build a random forest (RF) seepage risk prediction model. Second, the optimal branch and parameters are selected by the 5-fold cross-validation method to establish the RF prediction training model. Additionally, to illustrate the effectiveness of the method, the operational stage of Wuhan Metro Line 3 in China is taken as a case study. The results conclude that the segment spalling area, crack width, and loss rate of the rebar cross-section have a strong influence on WS. Finally, the test data are predicted, and the prediction result error index is calculated. Compared with the predictions of some traditional machine learning methods, such as support vector machines and artificial neural networks, RF prediction has the highest accuracy and is the closest to the true value, which demonstrates the accuracy of the model and its application potential.

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“…e ANN model is an important branch of the machine learning (ML) technique and is inspired by the human brain [45,46]. With the help of computer calculation, many problems including blast-induced rock movement [47][48][49], blast-induced overpressure [50], rockburst [51], flyrock [52], and rock fragmentation [53,54] can be solved by learning message from the input variables and using these messages to predict the output variables. After reviewing previous studies [55,56], multilayer perception (MLP) which is composed of input layers, hidden layers, and output layers is the best type of neural network among many artificial neural networks.…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Indirect Determination Approach of Blast‐Induced Ground Vibration Based on a Hybrid SSA‐Optimized GP‐Based Technique

Jiang¹,

Xu²,

Chen

et al. 2021

Advances in Civil Engineering

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The accurate determination of blast-induced ground vibration has an important significance in protecting human activities and the surrounding environment. For evaluating the peak particle velocity resulting from the quarry blast, a robust artificial intelligence system combined with the salp swarm algorithm (SSA) and Gaussian process (GP) was proposed, and the SSA was used to find the optimal hyperparameters of the GP here. In this regard, 88 datasets with 9 variables including the ratio of bench height to burden (H/B) and the ratio of spacing to burden (S/B) were selected as the input variables, while peak particle velocity (PPV) was measured. Then, an ANN model, an SVR model, a GP model, an SSA-GP model, and three empirical models were established, and the predictive performance was evaluated by using the root-mean-square error (RMSE), determination coefficient (R2), value account for (VAF), Akaike Information Criterion (AIC), Schwarz Bayesian Criterion (SBC), and the run time. After comparing, it is found that the proposed SSA-GP yielded a superior performance and the ratio of bench height to burden (H/B) was the most sensitive variable.

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Feasibility of the indirect determination of blast-induced rock movement based on three new hybrid intelligent models

Cited by 39 publications

References 54 publications

Remote Sensing–Based Urban Green Space Detection Using Marine Predators Algorithm Optimized Machine Learning Approach

Remote Sensing–Based Urban Green Space Detection Using Marine Predators Algorithm Optimized Machine Learning Approach

Risk Prediction and Diagnosis of Water Seepage in Operational Shield Tunnels Based on Random Forest

Indirect Determination Approach of Blast‐Induced Ground Vibration Based on a Hybrid SSA‐Optimized GP‐Based Technique

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