Data-Driven Smart Composite Reinforcement for Precast Concrete [supporting dataset]

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
• Resource Type:
  Dataset
• Right Statement:
  Public Domain
• Geographical Coverage:
  United States
• 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.
• Content Notes:
  As this dataset is preserved in a repository outside U.S. DOT control, as allowed by the U.S. DOT’s Public Access Plan (https://doi.org/10.21949/1503647) Section 7.4.2 Data, the NTL staff has performed NO additional curation actions on this dataset. This dataset has been curated to CoreTrustSeal's curation level "C. Initial Curation." To find out more information on CoreTrustSeal's curation levels, please consult their "Curation & Preservation Levels" CoreTrustSeal Discussion Paper" (https://doi.org/10.5281/zenodo.11476980). NTL staff last accessed this dataset at its repository URL on 2024-08-28. If, in the future, you have trouble accessing this dataset at the host repository, please email NTLDigitalSubmissions@dot.gov describing your problem. NTL staff will do its best to assist you at that time.
• Format:
  ZIP
• Funding:
  69A3552348333
• Collection(s):
  University Transportation Centers
• Main Document Checksum:
  urn:sha-512:10e80f620b6999d264265bb28d074d0e001778e056b113f0d34be1b08d54b8eac93f1137232e7032f9ad8e95fcf092aec5309868902468481e2123ee909c1724
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/89207/dot_89207_DS1.zip
• File Type:
  [ZIP - 154.47 MB ]