Research on concrete early shrinkage characteristics based on machine learning algorithms for multi-objective optimization

Wang, Jianqun; Liu, Heng; Sun, Junbo; Huang, Bo; Wang, Yufei; Zhao, Hongyu; Saafi, Mohamed; Wang, Xiangyu

doi:10.1016/j.jobe.2024.109415

Journal of Building Engineering

2024

DOI: 10.1016/j.jobe.2024.109415

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Research on concrete early shrinkage characteristics based on machine learning algorithms for multi-objective optimization

Jianqun Wang,

Heng Liu,

Junbo Sun

et al.

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2024

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Cited by 3 publications

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Artificial intelligence based microcracks research in 3D printing concrete

Zhao,

Jassmi,

Liu

et al. 2024

Construction and Building Materials

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Artificial intelligence based microcracks research in 3D printing concrete

Zhao,

Jassmi,

Liu

et al. 2024

Construction and Building Materials

View full text Add to dashboard Cite

Compensating for Concrete Shrinkage with High-Calcium Fly Ash

Barabanshchikov,

Krotova,

Usanova

2024

Buildings

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Shrinkage of concrete during hardening is a serious problem in attempts to maintain the integrity of concrete structures. One of the methods of combating shrinkage is compensating for it using an expansive agent. The purpose of this work is to develop and study an expanding agent to concrete, including high-calcium fly ash and calcium nitrate as an expansion activator. The content of Ca(NO3)2 can be used to regulate the degree of expansion of the additive itself during hydration and, accordingly, to control shrinkage, thus obtaining shrinkage-free or expanding concrete. Shrinkage–expansion deformations of concrete can also be regulated by the amount of expanding additive replacing part of the cement. With the Ca(NO3)2 content of fly ash being 10% or more, concrete experiences expansion in the initial stages of hardening. The transition of deformation through 0 to the shrinkage region occurs depending on the composition and content of the additive after 8–15 days of hardening. It has been established that replacing cement with pure fly ash with a curing period of 90 days or more has virtually no effect on the strength of concrete, either in bending or in compression. The use of an expanding additive containing 5–15% Ca(NO3)2 reduces the strength of concrete by an average of 9%, despite the fact that calcium nitrate is a hardening accelerator.

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A Review of Smart Camera Sensor Placement in Construction

Tian,

Li,

Zhu

et al. 2024

Buildings

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Cameras, with their low cost and efficiency, are widely used in construction management and structural health monitoring. However, existing reviews on camera sensor placement (CSP) are outdated due to rapid technological advancements. Furthermore, the construction industry poses unique challenges for CSP implementation due to its scale, complexity, and dynamic nature. Previous reviews have not specifically addressed these industry-specific demands. This study aims to fill this gap by analyzing articles from the Web of Science and ASCE databases that focus exclusively on CSP in construction. A rigorous selection process ensures the relevance and quality of the included studies. This comprehensive review navigates through the complexities of camera and environment models, advocating for advanced optimization techniques like genetic algorithms, greedy algorithms, Swarm Intelligence, and Markov Chain Monte Carlo to refine CSP strategies. Simultaneously, Building Information Modeling is employed to consider the progress of construction and visualize optimized layouts, improving the effect of CSP. This paper delves into perspective distortion, the field of view considerations, and the occlusion impacts, proposing a unified framework that bridges practical execution with the theory of optimal CSP. Furthermore, the roadmap for future exploration in the CSP of construction is proposed. This work enriches the study of construction CSP, charting a course for future inquiry, and emphasizes the need for adaptable and technologically congruent CSP approaches amid evolving application landscapes.

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Research on concrete early shrinkage characteristics based on machine learning algorithms for multi-objective optimization

Cited by 3 publications

References 50 publications

Artificial intelligence based microcracks research in 3D printing concrete

Artificial intelligence based microcracks research in 3D printing concrete

Compensating for Concrete Shrinkage with High-Calcium Fly Ash

A Review of Smart Camera Sensor Placement in Construction

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