[HTML][HTML] Machine learning in concrete science: applications, challenges, and best practices

Z Li, J Yoon, R Zhang, F Rajabipour… - npj computational …, 2022 - nature.com
Concrete, as the most widely used construction material, is inextricably connected with
human development. Despite conceptual and methodological progress in concrete science …

Prediction of concrete and FRC properties at high temperature using machine and deep learning: a review of recent advances and future perspectives

NF Alkayem, L Shen, A Mayya, PG Asteris, R Fu… - Journal of Building …, 2023 - Elsevier
Concrete structures when exposed to elevated temperature significantly decline their
original properties. High temperatures substantially affect the concrete physical and …

Fire-induced spalling of ultra-high performance concrete: A systematic critical review

M Amran, G Murali, N Makul, M Kurpińska… - … and Building Materials, 2023 - Elsevier
Ultra-high performance concrete (UHPC) is a novel concrete class characterized by a
compressive strength of more than 150 MPa. One of the most significant drawbacks of …

Strategy for preventing explosive spalling and enhancing material efficiency of lightweight ultra high-performance concrete

JX Lu, P Shen, Y Sun, CS Poon - Cement and Concrete Research, 2022 - Elsevier
The highly dense structure which causes the risk of explosive spalling is one of the major
limitations of using ultra high-performance concrete (UHPC). This study developed a …

Verifying domain knowledge and theories on Fire-induced spalling of concrete through eXplainable artificial intelligence

MK al-Bashiti, MZ Naser - Construction and Building Materials, 2022 - Elsevier
This paper adopts eXplainable Artificial Intelligence (XAI) to identify the key factors
influencing fire-induced spalling of concrete and to extract new insights into the …

[HTML][HTML] Machine learning for all! Benchmarking automated, explainable, and coding-free platforms on civil and environmental engineering problems

MZ Naser - Journal of Infrastructure Intelligence and Resilience, 2023 - Elsevier
One of the key challenges in fully embracing machine learning (ML) in civil and
environmental engineering revolves around the need for coding (or programming) …

[图书][B] Machine learning for civil and environmental engineers: A practical approach to data-driven analysis, explainability, and causality

MZ Naser - 2023 - books.google.com
Accessible and practical framework for machine learning applications and solutions for civil
and environmental engineers This textbook introduces engineers and engineering students …

Can domain knowledge benefit machine learning for concrete property prediction?

Z Li, T Pei, W Ying, WV Srubar III… - Journal of the …, 2024 - Wiley Online Library
Understanding and predicting process–structure–property–performance relationships for
concrete materials is key to designing resilient and sustainable infrastructure. While …

Hiding in plain sight: What can interpretable unsupervised machine learning and clustering analysis tell us about the fire behavior of reinforced concrete columns?

AÖ Çiftçioğlu, MZ Naser - Structures, 2022 - Elsevier
The role of machine learning (ML) continues to rise in the structural fire engineering area.
Noting the widespread of supervised ML approaches, such methods are being heavily …

What can we learn from over 1000 tests on fire-induced spalling of concrete? A statistical investigation of critical factors and unexplored research space

MK al-Bashiti, MZ Naser - Construction and Building Materials, 2023 - Elsevier
This paper presents a comprehensive statistical investigation of the largest database on fire-
induced spalling of concrete collected to date. In total, 1069 fire tests were collected and …