Optimized developed artificial neural network-based models to predict the blast-induced ground vibration

Shahri, Abbas Abbaszadeh; Asheghi, Reza

doi:10.1007/s41062-018-0137-4

Cited by 34 publications

(7 citation statements)

References 22 publications

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“…Sensitivity analysis is a key step in testing CNN performance and determining the impact of input parameters on the predictive results [38][39][40][41][42][43]. In order to study the influence of a series of CNN parameters on the prediction results and further optimize the established CNN, a sensitivity analysis was conducted.…”

Section: Effects Of Parameters On Classification Resultsmentioning

confidence: 99%

An Automatic Classification Method of Well Testing Plot Based on Convolutional Neural Network (CNN)

Chu

Dong

et al. 2019

Energies

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The precondition of well testing interpretation is to determine the appropriate well testing model. In numerous attempts in the past, automatic classification and identification of well testing plots have been limited to fully connected neural networks (FCNN). Compared with FCNN, the convolutional neural network (CNN) has a better performance in the domain of image recognition. Utilizing the newly proposed CNN, we develop a new automatic identification approach to evaluate the type of well testing curves. The field data in tight reservoirs such as the Ordos Basin exhibit various well test models. With those models, the corresponding well test curves are chosen as training samples. One-hot encoding, Xavier normal initialization, regularization technique, and Adam algorithm are combined to optimize the established model. The evaluation results show that the CNN has a better result when the ReLU function is used. For the learning rate and dropout rate, the optimized values respectively are 0.005 and 0.4. Meanwhile, when the number of training samples was greater than 2000, the performance of the established CNN tended to be stable. Compared with the FCNN of similar structure, the CNN is more suitable for classification of well testing plots. What is more, the practical application shows that the CNN can successfully classify 21 of the 25 cases.

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Section: Effects Of Parameters On Classification Resultsmentioning

confidence: 99%

An Automatic Classification Method of Well Testing Plot Based on Convolutional Neural Network (CNN)

Chu

Dong

et al. 2019

Energies

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“…For ANN models, hidden model layers resist definition or explicit explanation. However, according to previous works 28,[82][83][84] , ANNs with one or two hidden layer(s) can solve most problems. Therefore, a trial and error approach was conducted to find the best ANN models with one or two hidden layer(s).…”

Section: Case Studymentioning

confidence: 99%

Prediction of slope failure in open-pit mines using a novel hybrid artificial intelligence model based on decision tree and evolution algorithm

Bui

Nguyen

Choi

et al. 2020

Sci Rep

107

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In this study, the objective was to develop a new and highly-accurate artificial intelligence model for slope failure prediction in open-pit mines. For this purpose, the M5Rules algorithm was combined with a genetic algorithm (GA) in a novel hybrid technique, named M5Rules–GA model, for slope stability estimation and analysis and 450-slope observations in an open-pit mine in Vietnam were modeled using the Geo-Studio software based on essential parameters. The factor of safety was used as the model outcome. Artificial neural networks (ANN), support vector regression (SVR), and previously introduced models (such as FFA-SVR, ANN-PSO, ANN-ICA, ANN-GA, and ANN-ABC) were also developed for evaluating the proposed M5Rules–GA model. The evaluation of the model performance involved applying and computing the determination coefficient, variance account for, and root mean square error, as well as a general ranking and color scale. The results confirmed that the proposed M5Rules–GA model is a robust tool for analyzing slope stability. The other investigated models yielded less robust performance under the evaluation metrics.

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“…3. The developed methodology uses the following steps: literature review, experts' interviews, factor identification, estimating the weight of the factor by Simos (Wj) [21][22][23][24][25][26][27][28][29][30].…”

Section: Research Proceduresmentioning

confidence: 99%

Exploring the criteria influencing construction productivity of diaphragm walls in underground stations in Egypt

Meshref

Shehata

2020

Innov. Infrastruct. Solut.

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Planning and cost estimation engineers face great challenges to estimate construction productivity rate (PR) of diaphragm walls (DWs). A specific guideline to predict the construction of DWs in underground station is still not available. In order to overcome these challenges, the criteria affecting PR for the construction of DWs should be hence defined and the factors that impact the construction productivity of DWs should be well understood. Briefly and given the limitations of previous research work, we gathered and compiled a comprehensive key list of all criteria based on previous reports. This comprehensive key list was later reviewed by underground construction and specialized experts to define, identify and distinguish the most important criterion affecting the construction productivity of DWs in Egypt. We developed two questionnaire surveys to conduct these interviews with the experts. This obtained criterion was ranked and weighted using Simos' procedure based on prioritizing the influencing criteria. Our findings concluded that the ground condition type/and characteristics, soil-machine interaction, machine type and model characteristics (grab/or cutter), breakdowns and de-sender efficiency are significantly influencing the excavation rate. Additionally, factors such as cage type (fiber/or steel), connection type (welding/or mechanical) and volume of steel fixing crew are crucial to identify the rate of installation cages. On the other side, the arrival time of concrete car mixer and panel volume were determined to significantly impact the concrete casting rate. In summary, this study presented a distinctive weighting and ranking methodology of the influencing criteria for the PR of DWs. Furthermore, this work provides an initial concept for many full-scale prediction applications to estimate an accurate construction productivity of DWs, particularly in the underground projects.

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Optimized developed artificial neural network-based models to predict the blast-induced ground vibration

Cited by 34 publications

References 22 publications

An Automatic Classification Method of Well Testing Plot Based on Convolutional Neural Network (CNN)

An Automatic Classification Method of Well Testing Plot Based on Convolutional Neural Network (CNN)

Prediction of slope failure in open-pit mines using a novel hybrid artificial intelligence model based on decision tree and evolution algorithm

Exploring the criteria influencing construction productivity of diaphragm walls in underground stations in Egypt

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