Modeling resilient modulus of fine-grained materials using different statistical techniques

Khasawneh, Mohammad Ali; Al-jamal, Narjes Firas

doi:10.1016/j.trgeo.2019.100263

Cited by 36 publications

(19 citation statements)

References 37 publications

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“…More recently, some resilient models have been developed using machine learning and statistical techniques and optimization tools as described in the work developed by [73] and [74].…”

Section: Main Differences Of the Resilient Modulus Formulation-analysis And Comparisonmentioning

confidence: 99%

A geomechanics classification for the rating of railroad subgrade performance

Correia

Ramos

2021

Rail. Eng. Science

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The type of subgrade of a railroad foundation is vital to the overall performance of the track structure. With the train speed and tonnage increase, as well as environmental changes, the evaluation and influence of subgrade are even more paramount in the railroad track structure performance. A geomechanics classification for subgrade is proposed coupling the stiffness (resilient modulus) and permanent deformation behaviour evaluated by means of repeated triaxial loading tests. This classification covers from fine- to coarse-grained soils, grouped by UIC and ASTM. For this achievement, we first summarize the main models for estimating resilient modulus and permanent deformation, including the evaluation of their robustness and their sensitivity to mechanical and environmental parameters. This is followed by the procedure required to arrive at the geomechanical classification and rating, as well as a discussion of the influence of environmental factors. This work is the first attempt to obtain a new geomechanical classification that can be a useful tool in the evaluation and modelling of the foundation of railway structures.

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“…More recently, some resilient models have been developed using machine learning and statistical techniques and optimization tools as described in the work developed by [73] and [74].…”

Section: Main Differences Of the Resilient Modulus Formulation-analysis And Comparisonmentioning

confidence: 99%

A geomechanics classification for the rating of railroad subgrade performance

Correia

Ramos

2021

Rail. Eng. Science

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“…Furthermore, M r is used as a key input parameter for the mechanistic-empirical design of pavement structures to properly predict the deformation behavior of pavement foundation soils under traffic loads. As a result, an accurate prediction/determination of M r plays an important role in the economic and effective design of pavements in relation to post-construction cost, serviceability, durability, and service life ( 1 ). Therefore, M r has become the primary focus of many studies in recent years ( 1 – 15 ).…”

mentioning

confidence: 99%

“…As a result, an accurate prediction/determination of M r plays an important role in the economic and effective design of pavements in relation to post-construction cost, serviceability, durability, and service life ( 1 ). Therefore, M r has become the primary focus of many studies in recent years ( 1 – 15 ).…”

mentioning

confidence: 99%

“…M r can be determined by following three approaches: (1) performing advanced laboratory tests, (2) performing in situ tests for back-calculation of M r , and (3) developing models/correlations for M r ( 1 , 13 , 16 , 17 ). The first and second approaches are expensive, complex, and time-consuming ( 1 , 13 , 16 , 17 ). On the other hand, the third approach is a much simpler and more economical one ( 16 ).…”

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confidence: 99%

“…As an alternative to the constitutive models, in recent years, some studies have focused on developing artificial intelligence (AI) models to predict the M r of soils from soil physical properties and stress parameters ( 1 , 6 , 25 ). Ghorbani et al developed hybrid artificial neural network (ANN)–genetic algorithm models using 283 data to predict the M r of cohesive soils ( 6 ).…”

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confidence: 99%

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Developing New Empirical Formulae for the Resilient Modulus of Fine-Grained Subgrade Soils Using a Large Long-Term Pavement Performance Dataset and Artificial Neural Network Approach

Fedakar

2021

Transportation Research Record: Journal of the Transportation R

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Artificial neural network (ANN) has been successfully used for developing prediction models for resilient modulus (Mr). However, no reliable Mr formula derived from these models has been proposed in previous studies, although engineers/researchers need empirical formulae for hand calculation of Mr. Therefore, this study aimed to propose reliable empirical formulae for the Mr of fine-grained soils using ANN. For this purpose, thousands of ANN models were developed using the long-term pavement performance (LTPP) and external datasets. The input parameters were the percentage of soil particles passing through #200 sieve (P200), silt percentage (SP), clay percentage (CP), liquid limit (LL), plasticity index (PI), maximum dry density ([ρdry]max), optimum moisture content (wopt), confining pressure (σc), and nominal maximum axial stress (σz). The ANN models were compared with several constitutive models. The results indicate that the constitutive models failed to predict the Mr, and the best Mr predictions were obtained by the ANN-C9 (P200, SP, CP, LL, PI, σc, and σz), ANN-C10 (P200, SP, CP, [ρdry]max, wopt, σc, and σz), and ANN-C11 (P200, SP, CP, LL, PI, [ρdry]max, wopt, σc, and σz) models. Thus, the structures of these ANN models were formulated and proposed as the new empirical formulae for the Mr of fine-grained soils. Sensitivity analysis was also performed on these ANN models. It was determined that (ρdry)max is the most influential parameter in the ANN-C10 model, and LL is the most influential parameter in the ANN-C9 and ANN-C11 models. On the other hand, σc and σz are the least influential parameters.

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