A5032669797- Concrete and Cement Materials Research
- Innovative concrete reinforcement materials
- Magnesium Oxide Properties and Applications
- Infrastructure Maintenance and Monitoring
- Concrete Corrosion and Durability
- Occupational Health and Safety Research
- Structural Behavior of Reinforced Concrete
- Recycled Aggregate Concrete Performance
- Microbial Applications in Construction Materials
- BIM and Construction Integration
- Water Quality Monitoring Technologies
- Construction Project Management and Performance
- Hydrocarbon exploration and reservoir analysis
- Membrane Separation Technologies
- Innovations in Concrete and Construction Materials
- Structural Engineering and Vibration Analysis
- Methane Hydrates and Related Phenomena
- Construction Engineering and Safety
- Advancements in Battery Materials
- Graphene and Nanomaterials Applications
- Solar Thermal and Photovoltaic Systems
- Recycling and utilization of industrial and municipal waste in materials production
- Structural Load-Bearing Analysis
- Asphalt Pavement Performance Evaluation
- Hybrid Renewable Energy Systems
Cardiff Metropolitan University
2023-2024
King Fahd University of Petroleum and Minerals
2014-2023
Teesside University
2023
Hong Kong Polytechnic University
2023
Kabul University
2023
University of Petroleum
2023
University of Miami
2023
Digital Science (United States)
2023
Leeds Beckett University
2023
Universiti Sains Malaysia
2016-2018
Foamed concrete is special not only in terms of its unique properties, but also challenging compositional mixture design, which necessitates multiple experimental trials before obtaining the desired property like compressive strength. Regardless design challenges, artificial intelligence (AI) techniques have shown to be useful reliably estimating properties based on optimized proportions. This study proposes AI-based models predict strength foamed concrete. Three novel AI approaches, namely...
Large Language Models (LLMs) trained on large data sets came into prominence in 2018 after Google introduced BERT. Subsequently, different LLMs such as GPT models from OpenAI have been released. These perform well diverse tasks and gaining widespread applications fields business education. However, little is known about the opportunities challenges of using construction industry. Thus, this study aims to assess A critical review, expert discussion case validation are employed achieve study's...
The construction industry is a vital sector of the global economy, but it faces many productivity challenges in various processes, such as design, planning, procurement, inspection, and maintenance. Generative artificial intelligence (AI), which can create novel realistic data or content, text, image, video, code, based on some input prior knowledge, offers innovative disruptive solutions to address these challenges. However, there gap literature current state, opportunities, generative AI...
Bio-inspired self-healing of concrete cracks has been widely exploited to improve properties and thus increase life span using different bacterial species in recent years. The most common found the present literature are B. sphaericus, Sporosarcina pasteurii, Spore-forming alkali-resistant bacteria, megaterium subtilis, while there is no published research pseudomycoides heal cracks. Furthermore, need for more in-depth information on healing ratio deeper part crack remains. In study, a new...
An accurate calculation of the flexural capacity members is vital for safe and economical design FRP reinforced structures. The existing empirical models are not accurately calculating beams columns. This study investigated estimation using non-linear capabilities two Artificial Intelligence (AI) models, namely neural network (ANN) Random Forest (RF) Regression. were trained optimized hyperparameters obtained from trial-and-error method. coefficient correlation (R), Mean Absolute Error, Root...
In this article, detailed trials were undertaken to study the variation in genetic parameters order formulate more robust predictive models using gene expression programming (GEP) and multigene (MEP) for computing swelling pressure of expansive soils (Ps-ES). A total 200 datasets with ten input (i.e., clay fraction CF, liquid limit <a:math xmlns:a="http://www.w3.org/1998/Math/MathML" id="M1"><a:msub><a:mrow><a:mi>w</a:mi></a:mrow><a:mrow><a:mi>L</a:mi></a:mrow></a:msub></a:math> , plastic...
The study presents a sophisticated hybrid machine learning methodology tailored for predicting energy loads in occupied buildings. Leveraging eight pivotal input features—building compactness, surface area, wall roof overall height, orientation, glazing and area distribution—we elucidate the intricate relationships between building characteristics their corresponding heating load (HL) cooling (CL). We meticulously analyze these features across 12 diverse structural forms, each emblematic of...