A5001853147- Innovative concrete reinforcement materials
- Concrete and Cement Materials Research
- Infrastructure Maintenance and Monitoring
- Structural Behavior of Reinforced Concrete
- Recycled Aggregate Concrete Performance
- Advanced Machining and Optimization Techniques
- Fire effects on concrete materials
- Structural Response to Dynamic Loads
- Building materials and conservation
- Innovations in Concrete and Construction Materials
- Recycling and utilization of industrial and municipal waste in materials production
- Smart Materials for Construction
- Aquaculture Nutrition and Growth
- Flood Risk Assessment and Management
- Magnesium Oxide Properties and Applications
- Additive Manufacturing and 3D Printing Technologies
- Physiological and biochemical adaptations
- Advanced ceramic materials synthesis
- Concrete Corrosion and Durability
- Materials Engineering and Processing
- Hydrological Forecasting Using AI
- Hygrothermal properties of building materials
- Fire dynamics and safety research
- Hydrology and Watershed Management Studies
- Aquaculture disease management and microbiota
Southeast University
2020-2024
Universiti Tenaga Nasional
2023
Recently, the addition of natural fibers to high strength concrete (HSC) has been great interest in field construction materials. Compared artificial fibers, are cheap and locally available. Among all coconut have greatest known toughness. In this work, mechanical properties fiber reinforced (CFR-HSC) explored. Silica fume (10% by mass) super plasticizer (1% also added CFR-HSC. The influence 25 mm-, 50 75 mm-long 0.5%, 1%, 1.5%, 2% contents mass is investigated. microstructure CFR-HSC...
Researchers seek sustainable materials for eco-friendly cement and concrete to reduce CO2 emissions. This paper offers an extensive overview of the research conducted on technology with minimal zero-carbon review reviews technologies lowering construction industry's carbon footprint, focusing alternative binders supplementary cementitious (SCMs). Additionally, explores transformative potential capture utilization sustainability. It also explored life cycle assessments, economic aspects,...
Marble is currently a commonly used material in the building industry, and environmental degradation an inevitable consequence of its use. waste occurs during exploitation deposits using shooting technologies. The obtained elements most mainly often have irregular geometry small dimensions, which excludes their use stone industry. There no systematic way disposing these massive mounds waste, results occurrence landfills pollution. To mitigate this problem, effort was made to incorporate...
Recently, research has centered on developing new approaches, such as supervised machine learning techniques, that can compute the mechanical characteristics of materials without investing much effort, time, or money in experimentation. To predict 28-day compressive strength steel fiber-reinforced concrete (SFRC), i.e., individual and ensemble models, were considered. For this study, two approaches (SVR AdaBoost SVR bagging) one technique (support vector regression (SVR)) used. Coefficient...
Research has focused on creating new methodologies such as supervised machine learning algorithms that can easily calculate the mechanical properties of fiber-reinforced concrete. This research aims to forecast flexural strength (FS) steel concrete (SFRC) using computational approaches essential for quick and cost-effective analysis. For this purpose, SFRC data were collected from literature reviews create a database. Three ensembled models, i.e., Gradient Boosting (GB), Random Forest (RF),...
The ceramic waste powder (CWP) is generated in the industry during cutting and polishing stages. It harmful to environment needs a massive area for disposal. Therefore, an alternative way required reduce environmental pollution landfill caused by CWP. aim of study establish Artificial Intelligence (AI) model CWP concrete from experimental results save time cost. Advancements AI have made estimation mechanical characteristics possible employing Machine Learning (ML) approaches. In current...
This study evaluates the compressive strength (C–S) of nano-silica-based fiber-reinforced concrete (NS-FRC) by using advanced machine learning (ML) individual and ensembled techniques. The employed ML approaches used for analysis are Support Vector Machine (SVM), Multi-Layer Perceptron (MLP) eXtreme Gradient Boosting (XGB). Furthermore, level accuracy algorithms is also evaluated k-fold cross-validation technique. Statistical checks, i.e., root mean square error (RMSE), absolute (MAE)...
Water treatment plants produce a huge amount of sludge, which are ultimately disposed to the nearest water channel, leading harmful effects. This unmanaged wastewater plant sludge (WTS) results in social and environmental concerns. Therefore, utilization WTS construction activities can be viable option for management waste sustainable infrastructures. The main aim this study was investigate potential manufacturing clay bricks at an industrial scale. procured from Rawal Lake plant, Pakistan....
The effect of various parameters on the flexural strength (FS) ultra-high-performance concrete (UHPC) is an intricate mechanism due to involvement several inter-dependent raw ingredients. In this digital era, novel artificial intelligence (AI) approaches, especially machine learning (ML) techniques, are gaining popularity for predicting properties composites their better precision than typical regression models. addition, developed ML models in literature FS UHPC minimal, with limited input...
Machine learning and response surface methods for predicting the compressive strength of high-strength concrete have not been adequately compared. Therefore, this research aimed to predict (HSC) using different methods. To achieve purpose, neuro-fuzzy inference systems (ANFISs), artificial neural networks (ANNs), methodology (RSM) were used as ensemble Using an ANN ANFIS, output was modeled optimized a function five independent variables. The RSM designed with three input variables: cement,...
In today’s digital age, innovative artificial intelligence (AI) methodologies, notably machine learning (ML) approaches, are increasingly favored for their superior accuracy in anticipating the characteristics of cementitious composites compared to typical regression models. The main focus current research work is improve knowledge regarding application one new ML techniques, i.e., gene expression programming (GEP), anticipate ultra-high-performance concrete (UHPC) properties, such as...
High-strength concrete (HSC) is vulnerable to strength loss when exposed high temperatures or fire, risking the structural integrity of buildings and critical infrastructures. Predicting compressive HSC under high-temperature conditions crucial for safety. Machine learning (ML) techniques have emerged as a powerful tool predicting properties. Accurate prediction important can experience losses up 80% after exposure 800°C–1000°C. This study evaluates efficacy ML such Extreme Gradient...
Abstract Flood forecast models have become better through research as they led to a lower risk of flooding, policy ideas, less human death, and destruction property, so this study uses Scientometric analysis for floods. In analysis, citation-based data are used uncover major publishing areas, such the most prominent keywords, top best commonly publications, highly cited journal articles, countries, authors that achieved consequent distinction in flood analysis. Machine learning (ML)...
The estimation of concrete characteristics through artificial intelligence techniques is come out to be an effective way in the construction sector terms time and cost conservation. manufacturing Ultra-High-Performance Concrete (UHPC) based on combining numerous ingredients, resulting a very complex composite fresh hardened form. more along with possible combinations, properties relative mix proportioning, results difficult prediction UHPC behavior. main aim this research development Machine...
This research provides a comparative analysis of the optimization ultra-high-performance concrete (UHPC) using artificial neural network (ANN) and response surface methodology (RSM). By ANN RSM, yield UHPC was modeled optimized as function 22 independent variables, including cement content, compressive strength, type, strength class, fly-ash, slag, silica-fume, nano-silica, limestone powder, sand, coarse aggregates, maximum aggregate size, quartz water, super-plasticizers, polystyrene fiber,...