Theory-guided machine learning to predict density evolution of sand dynamically compacted under Ko condition

Tophel, Amir; Walker, Jeffrey P.; Dutta, Troyee Tanu; Kodikara, Jayantha

doi:10.1007/s11440-021-01431-2

Cited by 15 publications

(6 citation statements)

References 53 publications

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“…4, which reflects the hysteresis of SWRC and the dependence of SWRC on the volume changes. The typical predicted void ratio during compaction is compared with the laboratory test data documented by Tophel et al [13], as presented in Fig. 5 and Fig.…”

Section: Fig 2 Sketch Of the Compaction Process And The Simplified Lo...mentioning

confidence: 98%

“…The numerical simulations of the compaction process were conducted with several assumptions that were made by Tophel et al [13] to simplify the modelling process yet maintain the essential features of IC. The assumptions are as follows.…”

Section: Simulation Of Compaction and Traffic Loadsmentioning

confidence: 99%

“…Based on the aforesaid assumptions and the model proposed by Chen et al [4], various sets of numerical analyses with different ݁ and ܵ were conducted. For calibration and validation purposes, we selected the constant water content 1-D laboratory tests [13] which simulate the field compaction. The soil tested is a uniformly graded fine sand with ‫ܩ‬ ௦ =2.61.…”

Section: Fig 2 Sketch Of the Compaction Process And The Simplified Lo...mentioning

confidence: 99%

“…Good agreement between the predicted and test results can be seen in these two figures, demonstrating the effectiveness of the model in simulating compaction. [13] (where ݁ =0.789, ‫50.0=ݓ‬ and ܵ =0.17) Fig. 6.…”

Section: Fig 2 Sketch Of the Compaction Process And The Simplified Lo...mentioning

confidence: 99%

“…6. Simulation for compaction against the laboratory test results from Tophel et al [13] (where ݁ =0.772, ‫1.0=ݓ‬ and ܵ =0.34)…”

Section: Fig 2 Sketch Of the Compaction Process And The Simplified Lo...mentioning

confidence: 99%

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An unsaturated soil mechanics approach for performance-based intelligent compaction

et al. 2023

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Intelligent compaction involves using instrumented rollers to provide real-time monitoring of the compacted ground using sensors such as accelerometers and GPS. This technology has the potential to improve productivity and uniformity in construction but its advancement is currently impeded due toinaccurate estimation of the physical ground properties, such as dry density, and the absence of robust quantitative models to predict the effect of compaction on the long-term performance of unsaturated soils under repeated loads. In this study, the compaction of the soil layers and subsequent deformations under repeated traffic loads are simulated by using an advanced computational framework and model for unsaturated soils. By employing an effective stress concept, the presented computational approach allows a unified description of soils at various degrees of saturation. In addition, the model can capture plastic deformations at the initiation of loading and thereby offer accurate predictions of soil behaviour under cyclic loads. Several numerical examples will be provided to demonstrate how the initial states of compacted soils affect the compaction efficiency and the long-term performance of compacted soils.

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Section: Fig 2 Sketch Of the Compaction Process And The Simplified Lo...mentioning

confidence: 98%

Section: Simulation Of Compaction and Traffic Loadsmentioning

confidence: 99%

Section: Fig 2 Sketch Of the Compaction Process And The Simplified Lo...mentioning

confidence: 99%

Section: Fig 2 Sketch Of the Compaction Process And The Simplified Lo...mentioning

confidence: 99%

“…6. Simulation for compaction against the laboratory test results from Tophel et al [13] (where ݁ =0.772, ‫1.0=ݓ‬ and ܵ =0.34)…”

Section: Fig 2 Sketch Of the Compaction Process And The Simplified Lo...mentioning

confidence: 99%

See 3 more Smart Citations

An unsaturated soil mechanics approach for performance-based intelligent compaction

et al. 2023

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State-of-the-art review on the use of AI-enhanced computational mechanics in geotechnical engineering

Liu,

Su,

Sun

et al. 2024

Artif Intell Rev

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Significant uncertainties can be found in the modelling of geotechnical materials. This can be attributed to the complex behaviour of soils and rocks amidst construction processes. Over the past decades, the field has increasingly embraced the application of artificial intelligence methodologies, thus recognising their suitability in forecasting non-linear relationships intrinsic to materials. This review offers a critical evaluation AI methodologies incorporated in computational mechanics for geotechnical engineering. The analysis categorises four pivotal areas: physical properties, mechanical properties, constitutive models, and other characteristics relevant to geotechnical materials. Among the various methodologies analysed, ANNs stand out as the most commonly used strategy, while other methods such as SVMs, LSTMs, and CNNs also see a significant level of application. The most widely used AI algorithms are Artificial Neural Networks (ANN), Random Forest (RF), and Support Vector Machines (SVM), representing 35%, 19%, and 17% respectively. The most extensive AI application is in the domain of mechanical properties, accounting for 59%, followed by other applications at 16%. The efficacy of AI applications is intrinsically linked to the type of datasets employed, the selected model input. This study also outlines future research directions emphasising the need to integrate physically guided and adaptive learning mechanisms to enhance the reliability and adaptability in addressing multi-scale and multi-physics coupled mechanics problems in geotechnics.

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Real-time monitoring and quality assessment of subgrade compaction: key factors and ANN model

et al. 2022

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Theory-guided machine learning to predict density evolution of sand dynamically compacted under Ko condition

Cited by 15 publications

References 53 publications

An unsaturated soil mechanics approach for performance-based intelligent compaction

An unsaturated soil mechanics approach for performance-based intelligent compaction

State-of-the-art review on the use of AI-enhanced computational mechanics in geotechnical engineering

Real-time monitoring and quality assessment of subgrade compaction: key factors and ANN model

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