Initial state of excavated soil and rock (ESR) to influence the stabilisation with cement

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@Article{LU:2023:conbuildmat,

• author = "Yi Lu and Changhao Xu and Abolfazl Baghbani",
• title = "Initial state of excavated soil and rock ({ESR)} to influence the stabilisation with cement",
• journal = "Construction and Building Materials",
• volume = "400",
• pages = "132879",
• year = "2023",
• ISSN = "0950-0618",
• DOI = "doi:10.1016/j.conbuildmat.2023.132879",
• URL = "https://www.sciencedirect.com/science/article/pii/S0950061823025953",
• keywords = "genetic algorithms, genetic programming, Unconfined compressive strength, Excavated soil and rock, Cement, Artificial intelligence, Recycling, Sustainability, Tunnel construction",
• abstract = "This paper investigates the initial state of excavated soil and rock (ESR). These initial states include dry density, organic content, water content (Wc), cement content (Cc), liquid index (LI), dry or wet mixing method. Three ESRs collected from tunnelling projects and kaolin were used in this study to compare. The specimens (i.e., 50 mm in diameter and 100 mm in height) were prepared in the laboratory and cured at 7 and 14 days, and then assessed by the unconfined compressive strength (UCS) test. The analysis shows that the ratio of Wc/Cc is the primary factor to obtain different UCS for high LI ESR and a simple equation is proposed for quick prediction. For ESR with a more general LI, predictive equations are also proposed in terms of artificial neural network (ANN) and genetic programming (GP) for 7-days curing time. The results indicate that the both ANN models with Bayesian Regularization (BR) algorithm outperform ANN with Levenberg-Marquardt (LM) and GP model are accurate to predict UCS of mixtures",

}

Genetic Programming entries for Yi Lu Changhao Xu Abolfazl Baghbani

Citations