Author(s)
ie Chen,Jiaqing Shu, Shuang Xi, Xiaoqing Gu, Mingxing Zhu and Xiaojuan
Abstract
Predictive modelling has emerged as a critical approach for understanding the dynamic interaction between piles and soil, particularly under long-term cyclic loading scenarios. In engineering and construction, accurately forecasting how pile–soil systems respond to repeated loads is essential for ensuring the structural integrity of foundations. One of the key aspects influencing these models is stiffness degradation, a phenomenon where the rigidity of the pile or the soil diminishes over time due to fatigue, settlement, or cyclic stress.
Understanding stiffness degradation is pivotal for developing reliable infrastructure. Engineers must consider factors such as the material properties of piles, soil composition, and the environmental conditions that exacerbate this degradation. By integrating advanced mathematical and computational techniques, predictive models offer a robust framework to evaluate these variables comprehensively.
The models take into account the pile–soil relative stiffness, a fundamental parameter that affects load distribution and deformation patterns. As loading conditions change over time, relative stiffness can significantly impact how forces are transferred between the pile and the surrounding soil, influencing both safety and performance outcomes. Moreover, incorporating long-term cyclic loading data enhances the predictive accuracy, enabling more resilient design strategies.
In summary, the article provides a well-researched exploration of predictive modelling for pile–soil systems, emphasising the necessity of considering stiffness degradation. By refining these models and incorporating real-world variables, construction and geotechnical experts can improve forecasting capabilities, optimise designs, and mitigate risks associated with foundational failures.
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