Committee Mission: Develop and report information on machine learning and advanced statistical learning techniques for construction and design in the concrete community.

Goals: Increase awareness of ML in the concrete community by: 1. Developing publications relating to machine learning with cement-based materials. One focusing on “big picture” impact and challenges, one focusing on construction applications, and another focusing on structural design applications/code implications. 2. Collaborate with ACI committees on best practices to reuse data from the ACI DataCenterHub via ML (or a similarly-functioned data hub). 3. Collaborate with ACI committees to disseminate ML information and determine ways ML may be integrated into the concrete community. 4. Collaborate with technical organizations outside of ACI, e.g., ASTM, PCI, RILEM, ISO, ASCE (SEI, EMI) to facilitate and coordinate information sharing. Forster discussion on research needs and challenges preventing ML from wide adoption in the concrete community. 5. Develop guidelines to evaluate technology and potential code provisions for ML. 6. Organize ACI sessions to highlight the use of ML in the concrete industry.

Chair: Stephanie Paal

TAC Contact: Kamal Khayat