Potential anti-vibration pavements with damping layer: Finite element (FE) modeling, validation, and parametrical studies

Huang, Jiandong; Losa, Massimo; Leandri, Pietro; Kumar, Shiva G.; Zhang, Junfei; Sun, Yuantian

doi:10.1016/j.conbuildmat.2021.122550

Cited by 18 publications

(9 citation statements)

References 32 publications

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“…As mentioned above, compressive strength is a key index for evaluating the construction process and workability of cement-based composites, so most studies focus on experiments and revealing mechanisms [50][51][52][53][54][55][56][57]. Typically, the strength of cement-based materials is tested in the laboratory and the practical civil engineer finds the optimized proportion of the constituent materials in the composition [58][59][60][61][62][63][64][65][66]. However, experimental research is time-consuming and expensive and is not suitable for a large number of laboratory tests [41,42,[67][68][69][70][71][72].…”

Section: Introductionmentioning

confidence: 99%

Estimating the Compressive Strength of Cement‐Based Materials with Mining Waste Using Support Vector Machine, Decision Tree, and Random Forest Models

Liu

Zhang

et al. 2021

Advances in Civil Engineering

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To estimate the compressive strength of cement-based materials with mining waste, the dataset based on a series of experimental studies was constructed. The support vector machine (SVM), decision tree (DT), and random forest (RF) models were developed and compared. The beetle antennae search (BAS) algorithm was employed to tune the hyperparameters of the developed machine learning models. The predictive performances of the three models were compared by the evaluation of the values of correlation coefficient (R) and root mean square error (RMSE). The results showed that the BAS algorithm can effectively tune these artificial intelligence models. The SVM model can obtain the minimum RMSE, while the BAS algorithm is inefficient in DT and RF models. The SVM, DT, and RF models can be used to predict the compressive strength of cement-based materials using solid mining waste as aggregate effectively and accurately, with high R values and lower RMSE values. The RF algorithm can obtain the highest value of R and the lowest value of RMSE, demonstrating the highest accuracy. The solid mining waste to cement ratio is the most important variable to affect the compressive strength. Curing time was also an important parameter in the compressive strength of cemented materials, followed by the water-solid ratio of mining waste and fine sand ratio.

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Section: Introductionmentioning

confidence: 99%

Estimating the Compressive Strength of Cement‐Based Materials with Mining Waste Using Support Vector Machine, Decision Tree, and Random Forest Models

Liu

Zhang

et al. 2021

Advances in Civil Engineering

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“…e recent research showed that the mechanical properties (such as strength and stiffness) of engineering materials can be accurately predicted by using artificial intelligence technology, reducing the cost of expensive and cumbersome laboratory tests [16][17][18]. Moreover, the prediction of engineering materials by artificial intelligence can replace the traditional mathematical model and improve the calculation rate and accuracy [19][20][21][22][23][24][25][26][27][28][29].…”

Section: Introductionmentioning

confidence: 99%

Simulation of the Compressive Strength of Cemented Tailing Backfill through the Use of Firefly Algorithm and Random Forest Model

Wang

Jia

Huang

2021

Shock and Vibration

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Cemented tailings backfill is widely used in worldwide mining areas, and its development trend is increasing due to the technical and economic benefits. However, there is no reliable and simple machine learning model for the prediction of the compressive strength. In the present study, the research process to use artificial intelligence algorithms to predict the compressive strength of cemented tailing backfill was conducted, overcoming the shortcomings of traditional empirical formulas. Experimental tests to measure the compressive strength of cemented tailing backfill were conducted to construct the dataset for the machine learning. Five input parameters (tailing to cement ratio, percentage of fine tailings, cement type, curing time, and solid to water ratio) were considered for the design of the laboratory tests. The firefly algorithm (FA) was used to tune the random forest (RF) hyperparameters, and it was adopted to combine the RF model to improve the accuracy and efficiency for the prediction of the compressive strength of the cemented tailing backfill. By comparing the predicted and actual results, the reliability and accuracy of the prediction model proposed are confirmed. Tailing to cement ratio and curing time are the two most important parameters to the compressive strength of the cemented tailing backfill.

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“…e slope is not only a special geological environment but also an important part of engineering construction. Due to the construction demand for a large number of highway projects in recent years, the stability of the slope during the construction process is a link that cannot be ignored [11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

“…e stability analysis of these slopes mainly includes the judgment basis of slope instability, the stability of the excavated slope, the comparative analysis of the selection of different software, and the analysis of the internal force of the slope with the supporting structure [25,26]. In recent years, the FE method has been increasingly used to predict the displacement and stress in statically indeterminate slopes, dams, and embankments [5,13,14,[27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42][43][44][45].…”

Section: Introductionmentioning

confidence: 99%

Structural Analysis of Backfill Highway Subgrade on the Lower Bearing Capacity Foundation Using the Finite Element Method

Huang

et al. 2021

Advances in Civil Engineering

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The present study is to investigate the stability of the backfill subgrade on the lower bearing capacity foundation. A finite element (FE) model was constructed to simulate the high-filled road subjected to the actual self-weight load. The strength reduction method was adopted to establish an analysis model of slope stability. At the same time, the sensitivity analysis of the factors affecting the slope stability was carried out through parametric studies, including the elastic modulus, cohesion, internal friction angle, and slope rate. The results showed that the slope stability analysis model established by the strength reduction method can characterize the stability of the slope by calculating the slope safety coefficient. The mutation point of the relationship curve between the displacement generated in the slope and the reduction coefficient can be used as the criterion. Under the condition of a given strength reduction coefficient, the calculated results obtained through FE modeling can show the development of the equivalent plastic zone in the form of cloud diagrams.

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Potential anti-vibration pavements with damping layer: Finite element (FE) modeling, validation, and parametrical studies

Cited by 18 publications

References 32 publications

Estimating the Compressive Strength of Cement‐Based Materials with Mining Waste Using Support Vector Machine, Decision Tree, and Random Forest Models

Estimating the Compressive Strength of Cement‐Based Materials with Mining Waste Using Support Vector Machine, Decision Tree, and Random Forest Models

Simulation of the Compressive Strength of Cemented Tailing Backfill through the Use of Firefly Algorithm and Random Forest Model

Structural Analysis of Backfill Highway Subgrade on the Lower Bearing Capacity Foundation Using the Finite Element Method

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