Predicting uniaxial compressive strength of serpentinites through physical, dynamic and mechanical properties using neural networks

Moussas, Vassilios C.; Diamantis, Konstantinos

doi:10.1016/j.jrmge.2020.10.001

Cited by 26 publications

(3 citation statements)

References 48 publications

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“…As shown in Figure 4, the activation function of the hidden layer of the multi-layer perceptron is the hyperbolic tangent function (Aras et al, 2020;Moussas et al, 2021):…”

Section: Neural Network Analysis 41 Neural Network Model Trainingmentioning

confidence: 99%

Deterioration mechanism of mechanical properties of phosphorite under different saturation duration

Wang

Zhang

et al. 2023

Front. Earth Sci.

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In order to explore the deterioration mechanism of mechanical properties of phosphate rock under different saturation time, the degradation mechanism of phosphoric rock samples under different saturation duration was analyzed by laboratory test, theoretical analysis and neural network modeling, and the results is as follows: saturation of water will result in deterioration of mechanical properties of samples. The peak compressive strength and peak strain of the samples decreased gradually with the increase of saturation time. The average peak strength of 12, 24, and 36 h saturated specimens is 8.6%, 21.1%, and 32.2% lower than that of natural specimens, and the peak strain is 5.9%, 13.9%, and 31.3% lower, respectively. The stress-strain curves of the samples with water saturation for 36 h have more jitter stages after the peak, indicating that the plastic characteristics of the samples will be increased with water saturation for a long time. The neural network method was used to analyze the test parameters and the mechanical parameters of the samples, and the mechanical properties under the action of saturated water and confining pressure were obtained. The neural network model was established to represent the mechanical properties of the samples, and the average accuracy of the model was 0.89. The model can be used to predict and verify the mechanical properties of samples under other saturation and confining pressure conditions in the limited region. The research results can provide theoretical reference for the deterioration mechanism of confining pressure in water-rich roadway.

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“…As shown in Figure 4, the activation function of the hidden layer of the multi-layer perceptron is the hyperbolic tangent function (Aras et al, 2020;Moussas et al, 2021):…”

Section: Neural Network Analysis 41 Neural Network Model Trainingmentioning

confidence: 99%

Deterioration mechanism of mechanical properties of phosphorite under different saturation duration

Wang

Zhang

et al. 2023

Front. Earth Sci.

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“…Several related studies such as in determining foundation behavior like prediction of shallow foundation reliability [11], pile raft foundation [12], axial capacity of pile foundation [13], shaft resistance [14], elastic settlement [15], settlement shallow foundation [16] and loading-unloading pile static load [17]. Other related research such as predicting soil physical and mechanical properties like prediction of CBR value [18], uniaxial compressive strength [19], undrained shear strength [20]- [21], bearing capacity [22]- [23], unit weight [24], compression index & compression ratio [25], classification [26], compression coefficient [27], liquefaction [28], and electrical resistivity of soil [29]. ANN is also used in prediction of dynamic compaction [30] and slope stability [31].…”

Section: Table 1 Summarize Of Literature Reviewmentioning

confidence: 99%

Prediction of standard penetration test value on cohesive soil using artificial neural networks

Nugroho¹,

Fernando²,

Suryanita³

2021

Jurnal Informatika

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Soil investigation is the main key in starting a construction. Standard Penetration Test (SPT) and Cone Penetration Test (CPT) are field tests that are often used in estimating soil parameters for foundation design purposes. The SPT value shows a correlation with the CPT value and other soil parameters. At present, there have been many conventional correlations examining these correlations, but the nonlinear nature of the soil due to very complex soil formations means that this correlation cannot be used in all situations. Artificial neural networks (ANN) are often used to estimate a complex and nonlinear value. In this study, that will predict the value of SPT on cohesive soil based on CPT test data and soil physical properties using artificial neural network capabilities using the Backpropagation algorithm and the activation function is bipolar sigmoid. This study uses 284 data from several places in Sumatra Island, Indonesia with data input are tip resistance (qc), shaft resistance (fs), effective overburden pressure (σ'0), percentage of liquid limit, plastic limit, sand, silt and clay. This study shows that the artificial neural network is able and effective in predicting the N-SPT value with a small error value and a strong regression equation. In this study, RMSE 3,441, MAE 2,318 and R 2 0,9451 for training data and RMSE 2,785, MAE 2,085, R 2 0,9792 for test data. This model is hereinafter referred to as NN_Nspt(C).

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“…The process of excavation and stockpiling is an important problem because it involves the safety of people, equipment, and buildings around the slope (2). Therefore, before mining activities, it is necessary to conduct a study on slope stability and slope design that considers the safety aspect (3). Referring to the rules in mining science, the basic principle that must be understood in determining the design of a mine opening is to determine the optimal slope angle of the mine opening, in the sense of determining a stable overall slope angle to the mining end (4).…”

Section: Introductionmentioning

confidence: 99%

Slope stability analysis at coal mining sites in Central Kalimantan

Prabowo,

Barlian,

Syah

et al. 2023

IOP Conf. Ser.: Earth Environ. Sci.

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Slope stability is an important factor in the planning and operation of open pits and mines. Currently the company has been mining up to a height of 20 meters. In the implementation of mining activities, the potential for landslides on the side walls of the slopes of sector X occurs on one of the slopes, namely at an elevation of +70 masl. The company made two design choices, one of which was confirmed by researchers in overcoming the potential for landslides. In carrying out slope analysis, information about the general properties of rock mass consists of testing the physical properties of rocks and testing the mechanical properties of rocks. The results of the safety factor analysis of the A-A cross-section with 2.476 saturated MAT (Stable) and dry MAT 3.159 (Stable), while the B-B cross-section with saturated MAT 2.161 (Stable) and dry MAT MAT is 2.771 (Stable). Meanwhile, for the two segments, A-A’ with saturated MAT of 2,459 (Stable) and dry MAT of 3,131 (Stable), while the B-B’ section with saturated MAT of 1,653 (Stable) and dry MAT of 2,464 (Stable). The researcher recommends using option 1 based on the safety factor value which shows a higher value.

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Predicting uniaxial compressive strength of serpentinites through physical, dynamic and mechanical properties using neural networks

Cited by 26 publications

References 48 publications

Deterioration mechanism of mechanical properties of phosphorite under different saturation duration

Deterioration mechanism of mechanical properties of phosphorite under different saturation duration

Prediction of standard penetration test value on cohesive soil using artificial neural networks

Slope stability analysis at coal mining sites in Central Kalimantan

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