Machine learning for structural engineering: A state-of-the-art review
HT Thai - Structures, 2022 - Elsevier
Abstract Machine learning (ML) has become the most successful branch of artificial
intelligence (AI). It provides a unique opportunity to make structural engineering more …
intelligence (AI). It provides a unique opportunity to make structural engineering more …
Predictive models for concrete properties using machine learning and deep learning approaches: A review
Concrete is one of the most widely used materials in various civil engineering applications.
Its global production rate is increasing to meet demand. Mechanical properties of concrete …
Its global production rate is increasing to meet demand. Mechanical properties of concrete …
[HTML][HTML] Exploring temperature-resilient recycled aggregate concrete with waste rubber: An experimental and multi-objective optimization analysis
For low-carbon sustainability, recycled rubber particles (RPs) and recycled aggregate (RA)
could be used to make rubber-modified recycled aggregate concrete (RRAC). The …
could be used to make rubber-modified recycled aggregate concrete (RRAC). The …
[HTML][HTML] Explainable machine learning model and reliability analysis for flexural capacity prediction of RC beams strengthened in flexure with FRCM
This paper presents a data-driven approach to determine the load and flexural capacities of
reinforced concrete (RC) beams strengthened with fabric reinforced cementitious matrix …
reinforced concrete (RC) beams strengthened with fabric reinforced cementitious matrix …
Prediction of thermo-mechanical properties of rubber-modified recycled aggregate concrete
The recycled aggregate (RA) and waste rubber particles (RPs) can be combined to prepare
rubber-modified recycled aggregate concrete (RRAC) effectively contributing to low-carbon …
rubber-modified recycled aggregate concrete (RRAC) effectively contributing to low-carbon …
Machine learning applied to the design and inspection of reinforced concrete bridges: Resilient methods and emerging applications
W Fan, Y Chen, J Li, Y Sun, J Feng, H Hassanin… - Structures, 2021 - Elsevier
Abstract Machine learning is one of the key pillars of industry 4.0 that has enabled rapid
technological advancement through establishing complex connections among …
technological advancement through establishing complex connections among …
Reinforced concrete deep beam shear strength capacity modelling using an integrative bio-inspired algorithm with an artificial intelligence model
G Zhang, ZH Ali, MS Aldlemy, MH Mussa… - Engineering with …, 2022 - Springer
The design and sustainability of reinforced concrete deep beam are still the main issues in
the sector of structural engineering despite the existence of modern advancements in this …
the sector of structural engineering despite the existence of modern advancements in this …
[HTML][HTML] Squid game optimizer (SGO): A novel metaheuristic algorithm
Abstract In this paper, Squid Game Optimizer (SGO) is proposed as a novel metaheuristic
algorithm inspired by the primary rules of a traditional Korean game. Squid game is a …
algorithm inspired by the primary rules of a traditional Korean game. Squid game is a …
Shear capacity prediction of slender reinforced concrete structures with steel fibers using machine learning
OB Olalusi, PO Awoyera - Engineering Structures, 2021 - Elsevier
Shear failure in reinforced concrete beams poses a critical safety issue since it may occur
without any prior signs of damage in some cases. Many of the existing shear design …
without any prior signs of damage in some cases. Many of the existing shear design …
Time series analytics using sliding window metaheuristic optimization-based machine learning system for identifying building energy consumption patterns
Smart grids are a promising solution to the rapidly growing power demand because they can
considerably increase building energy efficiency. This study developed a novel time-series …
considerably increase building energy efficiency. This study developed a novel time-series …