Mix design and laboratory characterisation of rubberised mixture used as damping layer in pavements

Huang, Jiandong; Leandri, Pietro; Cuciniello, Giacomo; Losa, Massimo

doi:10.1080/10298436.2020.1869975

Cited by 22 publications

(12 citation statements)

References 39 publications

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“…e bounding and unbound pavement materials (including the surface layer, base layer, and subbase layer) were simplified to one type of material property [57]. Elastic behavior without plastic deformation characterized by Young's modulus and Poisson's ratio was adopted for the pavement materials.…”

Section: Materials Propertiesmentioning

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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Section: Materials Propertiesmentioning

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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“…Typically, researchers test the strength of cement-based materials in the laboratory to find the right mix for the best strength of cement-based materials. However, the laboratory experiment method has many shortcomings, which need to spend a lot of time, energy, and money [ 31 , 32 , 33 , 34 ]. In order to solve this problem, a more efficient and economical method is needed to predict the performance of cement-based materials.…”

Section: Introductionmentioning

confidence: 99%

Prediction of the Compressive Strength for Cement-Based Materials with Metakaolin Based on the Hybrid Machine Learning Method

et al. 2022

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Cement-based materials are widely used in construction engineering because of their excellent properties. With the continuous improvement of the functional requirements of building infrastructure, the performance requirements of cement-based materials are becoming higher and higher. As an important property of cement-based materials, compressive strength is of great significance to its research. In this study, a Random Forests (RF) and Firefly Algorithm (FA) hybrid machine learning model was proposed to predict the compressive strength of metakaolin cement-based materials. The database containing five input parameters (cement grade, water to binder ratio, cement-sand ratio, metakaolin to binder ratio, and superplasticizer) based on 361 samples was employed for the prediction. In this model, FA was used to optimize the hyperparameters, and RF was used to predict the compressive strength of metakaolin cement-based materials. The reliability of the hybrid model was verified by comparing the predicted and actual values of the dataset. The importance of five variables was also evaluated, and the results showed the cement grade has the greatest influence on the compressive strength of metakaolin cement-based materials, followed by the water-binder ratio.

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“…However, although these models have been applied in applications (e.g., in various software and engineering practices), their shortcomings were exposed in recent studies: many researchers challenged the accuracy of these models, particularly at the extreme high and low temperatures [ 4 , 6 , 25 , 26 , 27 ]. Yousefdoost et al [ 28 ] found that the models based on the parameter of viscosity can miscalculate the dynamic modulus, while those models based on the parameter of dynamic shear modulus of the asphalt binder can overvalue the dynamic modulus of the asphalt mixtures.…”

Section: Introductionmentioning

confidence: 99%

Developing Hybrid Machine Learning Models to Determine the Dynamic Modulus (E*) of Asphalt Mixtures Using Parameters in Witczak 1-40D Model: A Comparative Study

Huang

Yang

et al. 2022

Materials

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To characterize the dynamic modulus (E*) of the asphalt mixtures more accurately, a comparative study was shown in this paper, combining six ML models (BP, SVM, DT, RF, KNN, and LR) with the novelly developed MBAS (modified BAS, beetle antennae search) algorithm to check the potential to replace the empirical model. The hyperparameter tuning process of the six ML models by the proposed MBAS algorithm showed satisfactory results. The calculation and evaluation process demonstrated fast convergence and significantly lower values of RMSE for the five ML models (BP, SVM, DT, RF, and KNN) to determine the E* of the asphalt mixtures. Comparing the performances of the six ML models in the prediction of the E* by the statistical coefficients and Monte Carlo simulation, the RF model showed the highest accuracy, efficiency, and robustness.

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Mix design and laboratory characterisation of rubberised mixture used as damping layer in pavements

Cited by 22 publications

References 39 publications

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

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

Prediction of the Compressive Strength for Cement-Based Materials with Metakaolin Based on the Hybrid Machine Learning Method

Developing Hybrid Machine Learning Models to Determine the Dynamic Modulus (E*) of Asphalt Mixtures Using Parameters in Witczak 1-40D Model: A Comparative Study

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