Consolidation Grouting Quality Assessment using Artificial Neural Network (ANN)

Zadhesh, Jamal; Rastegar, F; Sharifi, Faraydoun; Amini, Hasel; Mirzaei-Nasirabad, Hossein

doi:10.1007/s40098-014-0116-4

Cited by 16 publications

(5 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Establishing the Evaluation Set of Each Index. For a specific index possibly obtained from some tests or measurements, its evaluation result can be classified into 1 , 2 , ⋅ ⋅ ⋅ , ( = 1, 2, 3 ⋅ ⋅ ⋅ ) in a set V (see (11)). Similarly, each element in the set V can be specified in a range ( Table 3).…”

Section: Fuzzymentioning

confidence: 99%

See 1 more Smart Citation

Evaluation of Ground Surface Pregrouting in a Mountain Tunnel Based on FAHP

Zhu

Sun

et al. 2019

Mathematical Problems in Engineering

View full text Add to dashboard Cite

The quality of ground surface pregrouting (GSPG) is commonly qualitatively evaluated using single factor; however, the quality evaluation involves numerous facets, and a quantitative evaluation is rare. The aim of this study was to quantitatively evaluate the quality of a GSPG based on three aspects. The fuzzy analytic hierarchy process (FAHP) was adopted to obtain the final score and quality classification of the GSPG. Based on three aspects, namely, integrity, continuity, and sturdiness, a series of field tests was also conducted, qualitatively evaluating the quality of the GSPG preliminary, to obtain the required test data and verify the FAHP results. The results of the FAHP showed that the quality of the GSPG was 86.42, which could be classified as “Good”, whereas the field tests exhibited that the GSPG was effective, thereby verifying that the FAHP was reliable. In addition, the proposed method provided a detailed comprehension of the quality evaluation of GSPG and a frame of reference for analogous engineering.

show abstract

Section: Fuzzymentioning

confidence: 99%

“…Typically, the quality is evaluated indirectly using various tests. At present, the water pressure test is a common method [9][10][11][12]. Other field tests such as ultrasonic tests have been commonly applied to the quality evaluation of grouting [13].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Ground Surface Pregrouting in a Mountain Tunnel Based on FAHP

Zhu

Sun

et al. 2019

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…Based on the data set of grouting drilling, Xie et al studied the case of water inrush in the grouting reinforcement of the working face and the data at the point of mine water inrush and combined it with the study of factors such as geological structures, hydraulic pressure of underlying water, floor waterconducting properties, and grouting amounts, and established the risk evaluation for floor water inrush in the grouted working face [16][17][18]. The novel technique of evaluating grouting techniques such as the artificial neural network (ANN) is also used for consolidation grouting quality assessment of dam foundation [19]. But the evaluation system of dam foundation is different from grouting of the working face floor.…”

Section: Introductionmentioning

confidence: 99%

Quantitative Identification of Grouting Effect of Working Face Floor with Multifactor Set

et al. 2022

View full text Add to dashboard Cite

The deep coal mining in the North China type of coalfield is generally threatened by the underlying limestone water of Taiyuan Formation and Ordovician. The occurrence of water inrush can be avoided effectively by applying grouting reinforcement technology to the coal floor. However, the reinforcement treatment of the coal floor belongs to underground concealment engineering, and it is of great significance for the safe production of the coal mine by scientifically and comprehensively evaluating the technical method of the grouting effect on the working face floor. In this study, the optimal transfer matrix is used to construct the judgment matrix that meets the consistency requirements; the analytic hierarchy process is improved; and the grouting effect of the working face floor is evaluated by fuzzy comprehensive evaluation based on several factors of the grouting effect. Taking the grouting engineering of the 15092 working face of the Guhanshan Mine as an example, the evaluation of the grouting effect based on four evaluatory indices have been refined: dynamic hydrological features, grout amount, grouting inspection hole, and geophysical prospecting have been refined. Based on the improved analytic hierarchy process (AHP), the result of the grouting effect can be divided into four levels: distinction, good, average, and poor. The study would play a very important role in the evaluation of the grouting reinforcement of the working face floor and the practice of coal mine production safety.

show abstract

“…Artificial Neural Network (ANN) is a research hotspot in the field of artificial intelligence since the 1980s. For the water conservancy project, ANN is a new and very important frontier research topic, and they have mature applications in many aspects including hydrological simulating, flood predicting, safety monitoring, and comprehensive evaluating [10][11][12][13][14][15][16][17][18][19]. Dawson et al [10] used the Radial Basis Function Neural Network (RBFNN) to simulate the local rainfall-runoff evolution of the Yangtze River in China, which promoted its development in the field of hydrological simulation.…”

Section: Introductionmentioning

confidence: 99%

“…Mata [14] combined the ANN model with the comprehensive evaluation system and proposed the corresponding method for evaluating the dam working behaviour. Zadhesh et al [15] adopted the ANN to estimate the secondary permeability required for the grouting quality assessment on the Cheraghvays dam's foundation. Unes et al [16] used the ANN to predict daily reservoir levels of the Millers Ferry Dam on the Alabama River in the USA.…”

Section: Introductionmentioning

confidence: 99%

Improving Dam Seepage Prediction Using Back-Propagation Neural Network and Genetic Algorithm

Zhang

Chen

2020

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Statistical model is a traditional safety diagnostic model for dam seepage. It can hardly display the nonlinear relationship between dam seepage and the load sets and has the disadvantage of poor extension prediction. In this paper, the theories of Back Propagation Neural Network (BPNN) combined with Genetic Algorithm (GA) are applied to the seepage prediction model. Taking a typical dam in China as an example, the prediction results of BPNN-GA model and statistical model are compared with the monitoring values. The results show that the improved dam seepage model enhances the ability of nonlinear mapping and generalization and makes the seepage prediction more accurate and reasonable in the near future. According to the established criterion, the safety state of the dam in flood season is evaluated.

show abstract

Consolidation Grouting Quality Assessment using Artificial Neural Network (ANN)

Cited by 16 publications

References 29 publications

Evaluation of Ground Surface Pregrouting in a Mountain Tunnel Based on FAHP

Evaluation of Ground Surface Pregrouting in a Mountain Tunnel Based on FAHP

Quantitative Identification of Grouting Effect of Working Face Floor with Multifactor Set

Improving Dam Seepage Prediction Using Back-Propagation Neural Network and Genetic Algorithm

Contact Info

Product

Resources

About