Multivariate correlation among resilient modulus and cone penetration test parameters of cohesive subgrade soils

Liu, Songyu; Zou, Haifeng; Cai, Guorong; Bheemasetti, Tejo V.; Puppala, Anand J.; Lin, Jun

doi:10.1016/j.enggeo.2016.05.018

Cited by 45 publications

(2 citation statements)

References 18 publications

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“…Some data points are classified as clayey silts or silt mixtures, and few are classified as sand mixtures or sands. This line of research has inspired comparable databases to be assembled in the literature recently (Müller et al 2014;Liu et al 2016). The availability of SPM2 (Soil Properties Manual version 2) as a freeware will hopefully encourage more data sharing and further enrichment of these databases to cover more parameters and/or more site conditions -http://140.112.10.150/fmanalysis.html?view=spm2 (Phoon and Ching 2017).…”

Section: Data Rich or Data Poor?mentioning

confidence: 99%

Managing Risk in Geotechnical Engineering – From Data to Digitalization

Phoon¹,

Ching²,

Yu³

2019

Proceedings of the 7th International Symposium on Geotechnical Safety and Risk (ISGSR 2019)

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If you scan a page from a soil report, this is called digitization. If you deploy digital technologies, both software such as building information modeling and machine learning and hardware such as autonomous drones and additive manufacturing, to support new and more collaborative forms of project delivery, this is called digitalization. Data lies at the heart of this transformation that is targeted at re-valuing infrastructure from a "brick and mortar" asset to a service for the interests of the end-users. There is a need to view the value of data completely differently from how they are routinely used in current practice. In particular, there is a need to treat data as assets in themselves, over and above their conventional roles as inputs to a physical model or as monitoring data to trigger interventions. This paper explores the availability and nature of geotechnical data and presents two recent advances made in this direction for a specific but important task of estimating soil/rock properties (compressive sampling and Bayesian machine learning). Data-driven decision making does not imply taking the engineer out of the entire life cycle management chain. It is intended to support rather than to replace human judgment.

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Section: Data Rich or Data Poor?mentioning

confidence: 99%

Managing Risk in Geotechnical Engineering – From Data to Digitalization

Phoon¹,

Ching²,

Yu³

2019

Proceedings of the 7th International Symposium on Geotechnical Safety and Risk (ISGSR 2019)

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“…However, these improvements were only achieved for particular soil types and site conditions, thereby limiting the applicability for wider use. In the attempt to assuage the effect of spatial variability, Liu et al [ 20 ] used data from different sites based on cone penetration test for model calibration and employed an advanced multivariate technique. Unfortunately, the predictions were found to be biased when a different dataset was applied.…”

Section: Introductionmentioning

confidence: 99%

Statistical analysis and prediction of spatial resilient modulus of coarse-grained soils for pavement subbase and base layers using MLR, ANN and Ensemble techniques

Ikeagwuani

Nwonu

2022

Innov. Infrastruct. Solut.

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Prediction methods for the estimation of the resilient modulus( ) of unbound granular materials continues to be fraught with challenges associated with the in situ dependency of the existing methods, which makes them unreliable when considering the spatial variability of soil properties. Moreover, the artificial intelligence methods, which prove to be superior for prediction, indicate that more recent and hybrid machine learning methods are more apt. Consequently, a comparative analysis was executed in the present study using advanced statistical approaches and a simple routine method for the prediction of the of coarse-grained soils used as unbound granular materials in highway pavements. This study considered also the spatial variability of the routine soil properties based on the long-term pavement performance database. The result of the descriptive analysis conducted using principal component analysis revealed that the variation in is due to three principal factors, which include the effect of moisture on the fines content, effect of coarse particles on the compaction characteristics and the effect of the soil stress state. Subsequently, prediction of the using multiple linear regression was unreliable based on residual analysis and multi-collinearity problems. Thus, predictive modeling of was then executed using artificial neural network (ANN) and ensemble techniques (random forest and gradient boosting machine), which resulted in very high R 2 values above 0.9, considered apt for MEPD. The ANN model was found to provide a superior prediction performance than the ensemble techniques.

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