A5042155262- Concrete and Cement Materials Research
- Innovative concrete reinforcement materials
- Innovations in Concrete and Construction Materials
- Recycled Aggregate Concrete Performance
- Magnesium Oxide Properties and Applications
- Additive Manufacturing and 3D Printing Technologies
- Recycling and utilization of industrial and municipal waste in materials production
- BIM and Construction Integration
- Concrete Properties and Behavior
- Transportation Safety and Impact Analysis
- Infrastructure Maintenance and Monitoring
- Concrete Corrosion and Durability
- Polymer Nanocomposites and Properties
- Corrosion Behavior and Inhibition
- Structural Behavior of Reinforced Concrete
- Building materials and conservation
- Cultural Heritage Materials Analysis
- Grouting, Rheology, and Soil Mechanics
- Structural Response to Dynamic Loads
- CO2 Sequestration and Geologic Interactions
- Scheduling and Timetabling Solutions
- Seismic and Structural Analysis of Tall Buildings
- Microbial Applications in Construction Materials
- Lubricants and Their Additives
- Scheduling and Optimization Algorithms
UNSW Sydney
2019-2024
The University of Melbourne
2013-2021
Islamic Azad University, Science and Research Branch
2019
University of Tehran
2018
Islamic Azad University North Tehran Branch
2018
Oregon State University
2015
Amirkabir University of Technology
2009-2012
Abstract The article presents a deep neural network model for the prediction of compressive strength foamed concrete. A new, high‐order neuron was developed to improve performance model. Moreover, cross‐entropy cost function and rectified linear unit activation were employed enhance present then applied predict concrete through given data set, obtained results compared with other machine learning methods including conventional artificial (C‐ANN) second‐order (SO‐ANN). To further validate...
Recycled aggregate concrete (RAC) enhanced with supplementary cementitious materials (SCMs) presents a sustainable alternative in the construction industry. This study involved comprehensive analysis of compressive strength (CS) RAC SCM, compiling dataset 3519 samples from published literature. A variety machine learning (ML) techniques were applied to predict RAC, including Elastic Net regression, K-Nearest Neighbor, Artificial Neural Network, Support Vector Machine, Decision Tree, Random...
This study addresses the enhanced prevalence of carbonation, a process accelerating steel reinforcement corrosion, in recycled aggregate concrete (RAC) compared to natural concrete. Traditional carbonation depth assessment methods RAC are noted for being labor-intensive, costly, and requiring specialized expertise. There's deficiency application machine learning techniques accurately predicting RAC, gap this aims fill. Utilizing extreme gradient boosting (XGBoost) technique, recognized its...
Abstract Reliable prediction of individual learning performance can facilitate timely support to students and improve the experience. In this study, two well‐known machine‐learning techniques, that is, vector machine (SVM) artificial neural network (ANN), are hybridized by teaching–learning‐based optimizer (TLBO) reliably predict student exam (fail‐pass classes final scores). For defined classification regression problems, TLBO algorithm carries out feature selection process both ANN SVM...
This study aims to investigate the possibility of using industrial and natural supplementary cementitious materials (SCMs) in self-consolidating concrete (SCC) with lower embodied carbon. Binary ternary blends ground granulated blast furnace slag (GGBFS) zeolite (NZ) at higher range replacements up 50 wt% Portland cement (PC) were used. Ten SCC mixtures various binder compositions evaluated for workability, compressive strength, durability properties, as well corresponding environmental...
This study presents a framework for designing low-carbon and cost-effective mixtures of recycled aggregate concrete (RAC) with supplementary cementitious materials by integrating machine learning grey wolf optimizer algorithms. The mix design process considers key performance parameters such as compressive strength, chloride ion penetration resistance, carbonation resistance. A dataset comprising 5306 data samples from 154 scientific resources is collected the literature to train models....