An intelligent model based on statistical learning theory for engineering rock mass classification

Liu, Kaiyun; Liu, Baoguo; Fang, Yu

doi:10.1007/s10064-018-1419-y

Cited by 43 publications

(18 citation statements)

References 30 publications

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“…Step 5: Perform the bacterial migration operation. When the number of replication operations reaches the maximum (N re max � 15 in this study), all bacteria in the current region are destroyed, and other regions are randomly selected to reexecute Step (1). is operation effectively avoids the local optimal solution and gives the BFOA a good global property.…”

Section: Bfoa Optimizes Lssvmmentioning

confidence: 99%

“…P and S represent the transverse and longitudinal wave intensities of geological prediction, respectively. (6) Strength resilience of rock mass e strength resilience value represents the compressive strength property of a material [1]. On the working face in tunnel engineering, the rebound strength of each section of the vault, spandrel, arch waist, arch feet, and the intersection of each position was measured to form a measurement group, and the data distribution law was statistically obtained.…”

Section: (3) Poisson's Ratiomentioning

confidence: 99%

“…where R c , K v , K 1 , K 2 , and K 3 represent rock hardness, rock mass integrity, groundwater development coefficient, occurrence of main weak structure surface, and initial stress state, respectively. ese parameters can be obtained through experiments or field observation after tunnel excavation, and the measurement method is referred to in the literature [1,17,29].…”

Section: Learning Sample Establishmentmentioning

confidence: 99%

“…Classification of surrounding rock is the basis of tunnel design and construction; a complete classification system should include two parts: a preconstruction survey classification and a modification classification during construction [1]. Limited by environmental conditions and technical means, rock classification in the survey stage produces only a relatively rough result.…”

Section: Introductionmentioning

confidence: 99%

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A New Reliability Rock Mass Classification Method Based on Least Squares Support Vector Machine Optimized by Bacterial Foraging Optimization Algorithm

Zheng

Jiang

Yang

et al. 2020

Advances in Civil Engineering

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Classification of the surrounding rock is the basis of tunnel design and construction. However, conventional classification methods do not allow dynamic tunnel construction adjustments because they are time-consuming and do not consider the randomness of rock mass. This paper presents a new reliability rock mass classification method based on a least squares support vector machine (LSSVM) optimized by a bacterial foraging optimization algorithm (BFOA). The LSSVM is adopted to express the implicit relationship between classification indicators and rock mass grades, which is a response surface function for reliability evaluation. LSSVM parameters were optimized by the BFOA to form a hybrid BFOA-LSSVM algorithm. Using geological prediction and rock strength resilience results as classification indicators, samples were developed to train the LSSVM model using the hybrid algorithm. The Monte Carlo sampling method of reliability classification was implemented and applied to the Suqiao tunnel at the Puyan highway in the Fujian province of China; the influence of parameters on the performance of the algorithm is discussed. The results indicate that the new method is feasible for tunnel engineering; it can improve the classification accuracy of surrounding rock exhibiting randomness, to provide an effective means of classifying surrounding rock in the dynamic design of tunnel construction.

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Section: Bfoa Optimizes Lssvmmentioning

confidence: 99%

Section: (3) Poisson's Ratiomentioning

confidence: 99%

Section: Learning Sample Establishmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A New Reliability Rock Mass Classification Method Based on Least Squares Support Vector Machine Optimized by Bacterial Foraging Optimization Algorithm

Zheng

Jiang

Yang

et al. 2020

Advances in Civil Engineering

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“…The rock mass is a concrete manifestation of the non-linear coupling of multiple factors in complex rock systems, which is directly related to the selection of construction design parameters and overall safety. The accurate assessment of rock mass quality reflects the physical and mechanical properties of the rock mass and provides reliable bases for engineering stability analysis, disaster prediction, prevention and control 1 . Therefore, it is necessary to develop appropriate methods to predict and evaluate the quality of rock mass 2 .…”

Section: Introductionmentioning

confidence: 99%

Rock mass classification prediction model using heuristic algorithms and support vector machines: a case study of Chambishi copper mine

Zhou

et al. 2022

Sci Rep

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The rock mass is one of the key parameters in engineering design. Accurate rock mass classification is also essential to ensure operational safety. Over the past decades, various models have been proposed to evaluate and predict rock mass. Among these models, artificial intelligence (AI) based models are becoming more popular due to their outstanding prediction results and generalization ability for multiinfluential factors. In order to develop an easy-to-use rock mass classification model, support vector machine (SVM) techniques are adopted as the basic prediction tools, and three types of optimization algorithms, i.e., particle swarm optimization (PSO), genetic algorithm (GA) and grey wolf optimization (GWO), are implemented to improve the prediction classification and optimize the hyper-parameters. A database was assembled, consisting of 80 sets of real engineering data, involving four influencing factors. The three combined models are compared in accuracy, precision, recall, F1 value and computational time. The results reveal that among three models, the GWO-SVC-based model shows the best classification performance by training. The accuracy of training and testing sets of GWO-SVC are 90.6250% (58/64) and 93.7500% (15/16), respectively. For Grades I, II, III, IV and V, the precision value is 1, 0.93, 0.90, 0.92, 0.83, the recall value is 1, 1, 0.93, 0.73, 0.83, and the F1 value is 1, 0.96, 0.92, 0.81, 0.83, respectively. Sensitivity analysis is performed to understand the influence of input parameters on rock mass classification. It shows that the sensitive factor in rock mass quality is the RQD. Finally, the GWO-SVC is employed to assess the quality of rocks from the southeastern ore body of the Chambishi copper mine. Overall, the current study demonstrates the potential of using artificial intelligence methods in rock mass assessment, rendering far better results than the previous reports.

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Machine Learning Approach to Predict Geology Ahead of Tunnel Boring Machine Face: Review and New Model

Landage,

Arora

2024

Springer Series in Geomechanics and Geoengineering

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An intelligent model based on statistical learning theory for engineering rock mass classification

Cited by 43 publications

References 30 publications

A New Reliability Rock Mass Classification Method Based on Least Squares Support Vector Machine Optimized by Bacterial Foraging Optimization Algorithm

A New Reliability Rock Mass Classification Method Based on Least Squares Support Vector Machine Optimized by Bacterial Foraging Optimization Algorithm

Rock mass classification prediction model using heuristic algorithms and support vector machines: a case study of Chambishi copper mine

Machine Learning Approach to Predict Geology Ahead of Tunnel Boring Machine Face: Review and New Model

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