open access publication

Article, 2024

Prediction of concrete abrasion depth and computational design optimization of concrete mixtures

Cement and Concrete Composites, ISSN 0958-9465, 1873-393X, Volume 148, Page 105431, 10.1016/j.cemconcomp.2024.105431

Contributors

Liu, Qiong 0000-0003-2288-6087 (Corresponding author) [1] Andersen, Lars Vabbersgaard 0000-0003-0543-2892 [1] Wu, Min 0000-0002-6775-2130 [1]

Affiliations

  1. [1] Aarhus University
    2. [NORA names: AU Aarhus University; University; Denmark; Europe, EU; Nordic; OECD]

Abstract

The failure of concrete for hydraulic structures due to the excessive abrasion depth has caused great economic losses. Despite the tremendous work that has been done, the durability design of hydraulic concrete structures concerning abrasion damage is still a difficult task, especially with a long service life aiming at economic benefit and environmental sustainability. In this paper, machine learning methodologies including random forests (RFs) and artificial neural networks (ANNs), are used to establish prediction models based on concrete mixture proportions, curing age, and hydraulic conditions. Furthermore, RF models are coupled with multiple objective particle swarm optimization (MOPSO) algorithms to perform computational design optimization of concrete mixtures. The optimal solutions are generated based on 5000 randomly generated mixture proportions, among which three optimal designs of concrete mixtures are selected to guide field applications on account of concrete durability requirements and economic benefits.

Keywords

MOPSO, RF model, abrasion, abrasion damage, abrasion depth, age, artificial neural network, benefits, computational design optimization, concrete, concrete mixture proportions, concrete mixtures, concrete structures, conditions, curing ages, damage, depth, design, design optimization, durability, durability design, durability requirements, economic benefits, economic losses, environmental sustainability, failure, failure of concrete, forest, hydraulic concrete structures, hydraulic conditions, hydraulic structures, learning methodology, life, long service life, loss, machine, machine learning methodology, methodology, mixture proportions, mixtures, model, network, neural network, optimal design, optimal solution, optimization, particle swarm optimization, particles, prediction, prediction model, proportion, random forest, requirements, service life, solution, structure, sustainability, swarm optimization, task

Funders

  • China Scholarship Council

Data Provider: Digital Science

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