Structurally, the lateral load-bearing capacity mainly depends on reinforced concrete (RC) walls. Determination of flexural strength and shear strength is mandatory when designing reinforced concrete walls. Typically, these strengths are determined t...
The meta-learning method proposed in this paper addresses the issue of small-sample regression in the application of engineering data analysis, which is a highly promising direction for research. By integrating traditional regression models with opti...
Japan's unique demographic trajectory, marked by population decline and aging, coupled with continued urbanization, presents distinct challenges for aligning built environment capacity with resource efficiency. This study aims to investigate the hist...
Optimized and automated methods for handling construction and demolition waste (CDW) are crucial for improving the resource recovery process in waste management. Automated waste recognition is a critical step in this process, and it relies on robust ...
We recently developed an explosion-propeller hybrid driving underwater robot combined with an AI-based concrete damage detection technique for concrete overhaul in real marine environments. Here, we describe steps for establishing a detection dataset...
The burgeoning demand for durable and eco-friendly road infrastructure necessitates the exploration of innovative materials and methodologies. This study investigates the potential of Graphene Oxide (GO), a nano-material known for its exceptional dis...
Foamed concrete (FC) is increasingly used in modern construction due to its lightweight nature, superior thermal insulation, and sustainable properties. However, accurately predicting its compressive strength remains a challenge due to the complex in...
This study focuses on modelling sustainable concretes' mechanical and environmental properties with interpretable artificial intelligence-based automated rule extraction, management of waste materials, and meeting future prospects. In this context, 2...
In light of the growing need to mitigate climate change impacts, this study presents an innovative methodology combining ensemble machine learning with experimental data to accurately predict the carbon dioxide footprint (CO-FP) of fly ash geopolymer...
This study proposed a data driven approach to predict the compressive strength (CS) of recycled aggregate concrete (RAC) for sustainable construction using an elite single genetic optimization algorithm-based cascade forward neural network (ESGA-CFNN...