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INVESTIGATIONS ON STATIC BEHAVIOR OF CONTINUOUS REINFORCED CONCRETE PAVEMENT USING FINITE ELEMENT ANALYSIS

Journal: Proceedings on Engineering Sciences (Vol.6, No. 3)

Publication Date:

Authors : ;

Page : 915-924

Keywords : Continuously reinforced concrete pavement; Static behavior; Finite element analysis; Load-carrying capacity; Stress distribution; Deflection characteristics;

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Abstract

Continuously Reinforced Concrete Pavement (CRCP) is a preferred choice in highway and roadway construction due to its durability and reduced maintenance needs. This study employs Finite Element Analysis (FEA) to investigate CRCP's static behavior, shedding light on its structural response. A comprehensive numerical model within a commercial software package is utilized, considering complex interactions among concrete, longitudinal reinforcement, and subgrade layers. This model accounts for nonlinear material behavior by incorporating concrete and steel reinforcement constitutive models. Various loading scenarios, including wheel loads and temperature variations, are simulated to assess their impact on CRCP's static behavior. The analysis focuses on essential performance metrics such as load-carrying capacity, stress distribution, and deflection patterns. It systematically examines variable parameters like reinforcement spacing, concrete strength, and slab thickness. The results are compared against experimental data and design guidelines to validate the numerical model's accuracy and applicability. This research enhances our understanding of CRCP's static behavior, emphasizing its sensitivity to different parameters. The findings underscore the significance of proper reinforcement spacing, concrete strength, and slab thickness in optimizing CRCP performance and longevity. The implications of this study extend to pavement designers, engineers, and transportation agencies involved in CRCP construction and maintenance. The validated numerical model serves as a valuable tool for optimizing designs, assessing structural integrity, and predicting long-term performance.

Last modified: 2024-09-02 03:22:19