A Deep Learning Model to Predict the Lateral Capacity of Monopiles

Taherkhani, Amir Hosein; Mei, Qipei; Han, Fei

doi:10.1061/9780784484685.023

Cited by 4 publications

(1 citation statement)

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“…Building on the growing emphasis in civil engineering literature on the effectiveness of prediction models for the performance behavior of various materials and systems [40,41], this research seeks to advance these methodologies, focusing specifically on alternative materials in RAMs. After performing laboratory tests, the experimental data were further used to develop prediction models of each property investigated with the aim of determining allowable contents of alternative materials in RAM that would satisfy required threshold values for two traffic levels: between 10 and 30 million ESALs and above 30 million ESALs.…”

Section: Prediction Model's Developmentmentioning

confidence: 99%

Performance Evaluation of Asphalt Mixtures Containing Different Proportions of Alternative Materials

Khorshidi,

Goli,

Orešković

et al. 2023

Sustainability

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With the increasing scarcity and cost of virgin materials for asphalt mixtures, the exploration of alternative components has intensified. Reclaimed asphalt pavement (RAP), crumb rubber (CR), steel slag (SS), and waste engine oil (WEO) have emerged as promising alternatives. Individually, RAP enhances rutting resistance but may compromise cracking tolerance; CR boosts cracking resistance; WEO affects cracking and rutting differently; and SS can influence moisture sensitivity. However, their combined impacts on asphalt performance, specifically on moisture damage, rutting, and cracking resistance, remain underexplored. In this study, 44 mixtures were assessed with varying RAP (0–75%), WEO (0–15%), and CR (0–15%) contents, alongside a constant SS aggregate (0% or 20%). The results indicate that specific combinations of these alternative materials can satisfy all performance thresholds for rutting, cracking, and moisture damage. To pinpoint ranges of optimal material contents for different high-traffic scenarios, prediction models were crafted using techniques like feed-forward neural network (FNN), generalized linear model (GLM), support vector regression (SVM), and Gaussian process regression (GPR). Among these, GPR demonstrated superior efficacy, effectively identifying regions of satisfactory performance.

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Section: Prediction Model's Developmentmentioning

confidence: 99%