Assessing external sulfate attack on thin-shell artificial reef structures under uncertainty. (1st July 2020)

• Record Type: Journal Article
• Authors: Yu, Yuguo; Gao, Wei; Castel, Arnaud; Liu, Airong; Chen, Xiaojun; Liu, Muyu
• Is Part Of: Ocean engineering. Volume 207(2020)
• Publication Date: 2020-07-01
• Subjects: Artificial reef -- Chemophysical modelling -- External sulfate attack -- Machine learning -- Uncertainty quantification; Ocean engineering -- Periodicals; Ocean engineering; Periodicals; 620.4162
• DOI: 10.1016/j.oceaneng.2020.107397
• Languages: English
• ISSNs: 0029-8018

Abstract

Abstract: Thin-shell artificial reef (AR) structures with spatial internal volumes have demonstrated superior stock recruitment ability and material efficiency than many gravity-based reef blocks, and cementitious materials, given the easy-to-tailor nature, remains the most popular in reef constructions to date. However, under constant seawater immersion, external sulfate attack (ESA) introduces a major and uncertain reliability concern to this type of AR system, due to the inherent material randomness. This study is concerned with developing a novel stochastic modelling framework for assessing the ESA under material uncertainty. In this paper, the main difficulty associated with the stochastic ESA modelling is identified for the first time, and a novel machine learning aided chemophysical modelling approach is proposed. The performance of the developed framework is carefully examined through the analyses on two types of cementitious materials under ESA. Highlights: The stochastic external sulfate attack (ESA) on artificial reef is concerned. The impact of inherent material uncertainty on stochastic ESA modelling is studied. The main difficulties in the stochastic ESA modelling is freshly identified. A novel machine learning aided stochastic modelling framework is developed. The effectiveness and efficiency of the proposed framework is carefully verified.