Data-Driven Smart Composite Reinforcement for Precast Concrete

Details

• Creators:
  Tao, Chengcheng ; Guan, Shanyue ; Li, Yongfei ; Lin, Yizhou ; Yan, Huaixiao ; Duan, Junyi ; Xiong, Xiaoli ; Wang, Sike
• Corporate Creators:
  Purdue University ; North Dakota State University
• Corporate Contributors:
  Transportation Infrastructure Precast Innovation Center (TRANS-IPIC) Tier-1 University Transportation Center (UTC) ; United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology
• Subject/TRT Terms:
  Composite Materials Costs Cracking Data Collection Datasets Finite Element Method Genetic Algorithms Optimization Precast Concrete Reinforced Concrete
• Publication/ Report Number:
  PU-23-RP-05
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Edition:
  Final Report: January 2024– November 2025
• Corporate Publisher:
  Transportation Infrastructure Precast Innovation Center (TRANS-IPIC) Tier-1 University Transportation Center (UTC)
• Abstract:
  The work aims to establish an integrated framework that transforms the design process of reinforced precast concrete (PC) by combining sensing technology, experimental characterization, multivariate numerical modeling, and multi-objective metaheuristic optimization. Embedded smart self-sensed composite rebar and advanced testing provided comprehensive dataset on material behavior and interaction mechanisms within reinforced PC components. These experimental insights were paired with high-fidelity finite element analysis (FEA) capable of capturing the coupled mechanical response of the system. It was found that FEA can be effectively used to construct a large-scale simulation database for reinforced concrete beam design. A metaheuristic genetic algorithm optimization, coupled with a random forest surrogate model, was employed to search for optimal solutions that satisfy four objectives: maximizing load capacity while minimizing total cost, deflection, and damage ratio. The resulting non-dominated solutions on the Pareto front provide meaningful trade-offs among strength, cost, stiffness, and damage ratio. This study enables virtual design and optimization for reinforced concrete beams within a wide design space and provides an intelligent pathway to optimize the reinforced concrete system based on user-defined criteria.
• Format:
  PDF
• Funding:
  69A3552348333
• Collection(s):
  University Transportation Centers
• Main Document Checksum:
  urn:sha-512:cba0d98691858b1fe812e38d83a1d719a14950a4a70f67811d0c245dd6def318b96e14d1049bfa6d22388ae00c021b1c679ba483daf8af93471b9eca872b8bd0
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/89201/dot_89201_DS1.pdf
• File Type:
  [PDF - 2.73 MB ]