[CivilEA Exclusive] ACI SP-350: The Concrete Industry in the Era of Artificial Intelligence

**poolmand** · 01-14-2024, 09:22 AM

ACI SP-350: The Concrete Industry in the Era of Artificial Intelligence

This special publication draws inspiration from the Technical Session entitled “The Concrete Industry in the Era of Artificial Intelligence,” held during the ACI Virtual Concrete Convention in spring 2020. To parallel the Technical Session, this special publication is also tailored to showcase the unprecedented potential of leveraging artificial intelligence (AI) methods—including its derivatives of machine learning (ML) and deep learning (DL)—in the concrete industry as a whole.

The idea behind this effort started as a thought during an ACI Committee 216 meeting. From there, both ACI Committees 444 (Chair: Thomas Schumacher) and 554 (Chair: Liberato Ferrara) displayed interest in co-sponsoring this special publication. This special publication comprises fifteen papers (five from our panelists and ten received from authors representing academia and the concrete industry). This collection of papers covers the use of various AI techniques at the material level (i.e., concrete performance and mass-scale testing, property predictions, and optimization, etc.), elemental level (e.g., behavioral and capacity prediction of slabs, walls, beams, and anchorages, etc.), as

well as system level (viz. damage and crack detection of concrete bridges and concrete composite structures).

We are very thankful to ACI, the ACI Technical Activities Committee, as well as all three technical committees. Your kind support and commitment have not only allowed us to explore a new realm of possibilities but have also enabled us to set the stage towards a new and modern future to our industry. Special thanks go to our panelists and contributors who were very kind to share their most recent research and unique ideas pertaining to infusing AI solutions to various problems within our domain. In addition, we send our warm regards to our reviewers, ACI staff, and Ms. Barbara A. Coleman for her help in setting up and editing this effort.

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