4.6 Article

Flexural Strength Prediction of Steel Fiber-Reinforced Concrete Using Artificial Intelligence

Related references

Note: Only part of the references are listed.
Article Construction & Building Technology

Effect of recycled tyre steel fiber on flexural toughness, residual strength, and chloride permeability of high-performance concrete (HPC)

Babar Ali et al.

Summary: Recycled tyre steel fiber (RSF) can be used as a fiber reinforcement to improve the ductility of high-performance concrete (HPC). In comparison with manufactured steel fiber (MSF), RSF shows higher effectiveness in terms of splitting-tensile and flexural-tensile strength, while the chloride permeability of RSF-reinforced concrete is similar to that of MSF-reinforced concrete.

JOURNAL OF SUSTAINABLE CEMENT-BASED MATERIALS (2023)

Article Environmental Sciences

Development of environment-friendly and ductile recycled aggregate concrete through synergetic use of hybrid fibers

Babar Ali

Summary: The substitution of natural aggregates with recycled ones can mitigate the negative impact of concrete industry on the environment. This study aimed to enhance the ductility of recycled aggregate concrete (RAC) by introducing hybrid fibers. The results showed that RAC with hooked steel fibers (HSF) and hybrid HSF-polypropylene fibers (PPF) exhibited superior flexural behavior, tensile strength, and compressive strength compared to plain natural aggregate concrete (NAC). Hybrid fibers were found to be more efficient than singular HSF in improving the mechanical properties of both RAC and NAC. The addition of 0.85% HSF + 0.15% PPF improved the water impermeability of concrete and reduced the water absorption capacity of RAC by 6.4%.

ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH (2022)

Article Construction & Building Technology

Hybrid fiber concrete with different basalt fiber length and content

Mehran Khan et al.

Summary: The study explored the influence of basalt fiber length and content on compressive and fracture parameters of hybrid fiber reinforced concrete (HyFRC). Results indicated that basalt fibers of different lengths and contents showed a great potential in enhancing the compressive and fracture properties of HyFRC.

STRUCTURAL CONCRETE (2022)

Article Chemistry, Physical

Machine Learning Prediction Models to Evaluate the Strength of Recycled Aggregate Concrete

Xiongzhou Yuan et al.

Summary: This research predicts the compressive and flexural strengths of recycled aggregate concrete (RAC) using ensemble machine learning methods. The random forest approach outperforms gradient boosting in predicting RAC strength, demonstrating higher precision.

MATERIALS (2022)

Article Chemistry, Physical

Optimization-Based Economical Flexural Design of Singly Reinforced Concrete Beams: A Parametric Study

Rizwan Azam et al.

Summary: Many studies have been conducted on the optimization of reinforced concrete structures, and this study demonstrates the potential of using the Solver tool in MS Excel for optimizing the design of simply supported RC beams. The results show that an economical design can be obtained by using this tool and considering different design variables. The study also explores the influence of different design variables on the economical design.

MATERIALS (2022)

Article Polymer Science

Forecasting the Mechanical Properties of Plastic Concrete Employing Experimental Data Using Machine Learning Algorithms: DT, MLPNN, SVM, and RF

Afnan Nafees et al.

Summary: With the increase in population and plastic waste, the construction industry is in need of solutions that promote sustainability. This study utilizes machine learning algorithms to create modeling tools for estimating the compressive and tensile strengths of plastic concrete, aiming to address the global concern of recycling plastic waste in construction.

POLYMERS (2022)

Article Polymer Science

Evaluation of the Strength of Slab-Column Connections with FRPs Using Machine Learning Algorithms

Nermin M. Salem et al.

Summary: Machine learning models can accurately and reliably model the behavior of slab-column connections with FRPs, and can predict their strength. Among the models examined, the ensembled boosted tree model showed the best performance, with the highest prediction accuracy and the lowest error.

POLYMERS (2022)

Article Crystallography

Predicting the Splitting Tensile Strength of Recycled Aggregate Concrete Using Individual and Ensemble Machine Learning Approaches

Yongzhong Zhu et al.

Summary: The application of waste materials in concrete for sustainable development is gaining popularity, and using machine learning algorithms such as the Bagging model can accurately predict the splitting tensile strength of recycled aggregate concrete.

CRYSTALS (2022)

Article Construction & Building Technology

Compressive strength prediction of fly ash-based geopolymer concrete via advanced machine learning techniques

Ayaz Ahmad et al.

Summary: This study utilized machine learning algorithms to predict the compressive strength of fly ash-based geopolymer concrete, with the bagging model showing superior performance in result prediction. Sensitivity analysis was conducted to determine the contribution of each parameter towards result prediction.

CASE STUDIES IN CONSTRUCTION MATERIALS (2022)

Article Chemistry, Physical

Compressive Strength of Steel Fiber-Reinforced Concrete Employing Supervised Machine Learning Techniques

Yongjian Li et al.

Summary: Recent research focuses on developing new approaches, like supervised machine learning techniques, to compute materials' mechanical characteristics with less effort, time, and money. In predicting 28-day compressive strength of steel fiber-reinforced concrete, ensemble methods (SVR AdaBoost and SVR bagging) and an individual technique (SVR) were used, with SVR AdaBoost showing the best performance. Through R-2, statistical assessment, and k-fold cross validation tests, these methods were found to perform better in predicting outcomes.

MATERIALS (2022)

Article Construction & Building Technology

Predicting Compressive Strength of Blast Furnace Slag and Fly Ash Based Sustainable Concrete Using Machine Learning Techniques: An Application of Advanced Decision-Making Approaches

Syyed Adnan Raheel Shah et al.

Summary: The utilization of waste industrial materials such as Blast Furnace Slag (BFS) and Fly Ash (F. Ash) has been proven to be an effective alternative strategy for producing eco-friendly and sustainable concrete production. In this study, artificial neural networks (ANNs) and decision trees (DTs) were used to predict the compressive strength of concrete, and both models showed strong correlation and high accuracy in predicting the strength based on eight factors. The findings suggest that these models can be used to train and predict the compressive strength of high-performance concrete.

BUILDINGS (2022)

Article Construction & Building Technology

Structural Performance of GFRP Bars Based High-Strength RC Columns: An Application of Advanced Decision-Making Mechanism for Experimental Profile Data

Muhammad Kashif Anwar et al.

Summary: Previous studies have shown that the use of GFRP bars can alleviate the issue of reinforced steel rusting in different concrete structures. However, the lack of a theoretical model has limited the confidence level in using GFRP bars in concrete columns. This study introduces a novel prediction model for the axial capability of GFRP-based concrete columns using data envelopment analysis and artificial neural networks. The proposed models demonstrate good correlation with the testing dataset, accurately forecasting the structural response of GFRP-made RC column structures.

BUILDINGS (2022)

Article Construction & Building Technology

Fiber-Type Influence on the Flexural Behavior of RC Two-Way Slabs with an Opening

Haleem K. Hussain et al.

Summary: Combining fiber with concrete can improve the strength of structural concrete elements. Different types and shapes of fibers have varying effects on the flexural behavior of two-way slabs. Steel fibers (hooked-end and corrugated) significantly enhance the flexural strength of reinforced concrete slab, while polyolefin fiber provides slight improvement in mechanical properties and flexural capacity.

BUILDINGS (2022)

Article Computer Science, Interdisciplinary Applications

A novel artificial intelligence technique to predict compressive strength of recycled aggregate concrete using ICA-XGBoost model

Jin Duan et al.

Summary: Recycled aggregate concrete is being researched using artificial intelligence techniques to assess its compressive strength, with the ICA-XGBoost model proving to be the most effective among the developed models. This model can be utilized in construction engineering to ensure adequate mechanical performance and safe usage of recycled aggregate concrete for building purposes.

ENGINEERING WITH COMPUTERS (2021)

Article Construction & Building Technology

Effectiveness of multiscale hybrid fiber reinforced cementitious composites under single degree of freedom hydraulic shaking table

Mingli Cao et al.

Summary: This study evaluated the properties of steel fibers, PVA fibers, and calcium carbonate whisker hybrid FRCC using a single degree of freedom hydraulic shaking table. Different mix proportions and fiber lengths were considered, and it was found that multiscale hybrid fibers offer resistances against crack growth under dynamic load, leading to improved displacement, strain, and acceleration properties.

STRUCTURAL CONCRETE (2021)

Article Engineering, Multidisciplinary

Improvement of boundary effect model in multi-scale hybrid fibers reinforced cementitious composite and prediction of its structural failure behavior

Chaopeng Xie et al.

Summary: The study determined the actual mechanical properties of MHFRCCs, predicted the structural failure behavior, analyzed the microstructure, and fracture process of the composite materials. This multi-scale hybrid fiber reinforced cementitious composites exhibit superior crack resistance performance, showing potential for structural applications in civil engineering.

COMPOSITES PART B-ENGINEERING (2021)

Article Green & Sustainable Science & Technology

Predictive modeling for sustainable high-performance concrete from industrial wastes: A comparison and optimization of models using ensemble learners

Furqan Farooq et al.

Summary: This study uses machine intelligence algorithms to predict the strength of high-performance concrete (HPC) prepared with waste materials, employing ensemble learners and weak learners to construct a robust model. Eight parameters were chosen to predict the output, and bagging and boosting learners were used to enhance the response of individual models.

JOURNAL OF CLEANER PRODUCTION (2021)

Article Chemistry, Physical

Predictive Modeling of Mechanical Properties of Silica Fume-Based Green Concrete Using Artificial Intelligence Approaches: MLPNN, ANFIS, and GEP

Afnan Nafees et al.

Summary: This study aims to create modeling tools for estimating the compressive and cracking tensile strengths of silica fume concrete. Using a broad and accurate database, machine learning models achieve high prediction accuracy.

MATERIALS (2021)

Article Chemistry, Physical

Application of Advanced Machine Learning Approaches to Predict the Compressive Strength of Concrete Containing Supplementary Cementitious Materials

Waqas Ahmad et al.

Summary: This study utilized supervised machine learning techniques to predict the compressive strength of concrete, with the bagging model showing the best performance. Sensitivity analysis was conducted to determine the contribution of each parameter. Machine learning techniques show potential for predicting concrete properties effectively.

MATERIALS (2021)

Article Construction & Building Technology

Effect of dispersed micro-fibers on tensile behavior of uncoated carbon textile-reinforced cementitious mortar after high-temperature exposure

Ngoc Hieu Dinh et al.

Summary: The study investigated the effect of adding micro-fibers on the tensile behavior of carbon textile-reinforced mortar (TRM) under high-temperature conditions, demonstrating that micro-fibers significantly improved the tensile characteristics of TRM specimens, especially at high temperatures. Amorphous metallic fibers showed advantages in enhancing crack stress after exposure to 200 degrees C compared to steel fibers.

CEMENT & CONCRETE COMPOSITES (2021)

Article Construction & Building Technology

Effects of fiber type and specimen thickness on flexural behavior of ultra-high-performance fiber-reinforced concrete subjected to uniaxial and biaxial stresses

Hyun-Oh Shin et al.

Summary: This study found that twisted steel fibers are more effective than straight steel fibers under biaxial flexural stress, but UHPFRC exhibited the best flexural performance when using straight steel fibers under both uniaxial and biaxial stress states.

CASE STUDIES IN CONSTRUCTION MATERIALS (2021)

Article Chemistry, Physical

Uniaxial Tensile Behavior, Flexural Properties, Empirical Calculation and Microstructure of Multi-Scale Fiber Reinforced Cement-Based Material at Elevated Temperature

Li Li et al.

Summary: This study investigated the uniaxial tensile and flexural properties of multi-scale fiber reinforced cement matrix composites (MSFRCs) under high temperatures, revealing a decrease in some properties with increasing temperature, but a slower decline at 600 degrees Celsius. The uniaxial tensile properties were found to be more sensitive to temperature and degraded more rapidly. The multi-scale hybrid fiber system significantly alleviated the deterioration of the mechanical properties of the cement-based composites under high temperatures.

MATERIALS (2021)

Article Construction & Building Technology

Effect of silica-fume content on performance of CaCO3 whisker and basalt fiber at matrix interface in cement-based composites

Mehran Khan et al.

Summary: The addition of silica fume is effective in enhancing the bond strength and mechanical properties at the fiber-matrix interface of FRCC. SEM analysis showed improved bond, while EDS energy spectrum indicated an increase in Si content leading to enhanced interface bond strength.

CONSTRUCTION AND BUILDING MATERIALS (2021)

Article Chemistry, Physical

Multivariable Regression Strength Model for Steel Fiber-Reinforced Concrete Beams under Torsion

Ahmed F. Deifalla et al.

Summary: This study investigates the torsional behavior and analysis of SFRC beams by compiling data from 210 beams tested under torsion from around the world. New models based on existing formulations and modified ACI design code are proposed to provide more accurate and reliable predictions for the torsional capacity of SFRC beams compared to existing design models. The experimental results show that the proposed models have better compliance and consistency with the test results.

MATERIALS (2021)

Article Construction & Building Technology

Compressive Strength Prediction via Gene Expression Programming (GEP) and Artificial Neural Network (ANN) for Concrete Containing RCA

Ayaz Ahmad et al.

Summary: The utilization of recycled coarse aggregate in concrete is an effective way to reduce environmental pollution, but the presence of adhered mortar on its surface affects its properties. A suitable mix design can enable the coarse aggregate to achieve the desired strength and be used in various construction projects. Employing supervised machine learning algorithms, gene expression programming, and artificial neural network can effectively predict the compressive strength of concrete.

BUILDINGS (2021)

Article Construction & Building Technology

Machine learning study of the mechanical properties of concretes containing waste foundry sand

Ali Behnood et al.

CONSTRUCTION AND BUILDING MATERIALS (2020)

Article Materials Science, Multidisciplinary

Estimating strength properties of geopolymer self-compacting concrete using machine learning techniques

Paul O. Awoyera et al.

JOURNAL OF MATERIALS RESEARCH AND TECHNOLOGY-JMR&T (2020)

Proceedings Paper Construction & Building Technology

Effects of water-cement ratio and notches to the flexural strength of concrete

Mohamad Shazwan Ahmad Shah et al.

4TH INTERNATIONAL CONFERENCE ON CONSTRUCTION AND BUILDING ENGINEERING & 12TH REGIONAL CONFERENCE IN CIVIL ENGINEERING (ICONBUILD & RCCE 2019) (2020)

Article Multidisciplinary Sciences

Efficiency of Supplementary Cementitious Materials and Natural Fiber on Mechanical Performance of Concrete

Sohaib Arshad et al.

ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING (2020)

Article Construction & Building Technology

Improvement in concrete behavior with fly ash, silica-fume and coconut fibres

Mehran Khan et al.

CONSTRUCTION AND BUILDING MATERIALS (2019)

Article Chemistry, Physical

Cyclic Response of Steel Fiber Reinforced Concrete Slender Beams: An Experimental Study

Constantin E. Chalioris et al.

MATERIALS (2019)

Article Engineering, Civil

Experimental study and modeling of fiber volume effects on frost resistance of fiber reinforced concrete

Mahmoud Nili et al.

INTERNATIONAL JOURNAL OF CIVIL ENGINEERING (2018)

Article Construction & Building Technology

Evaluation of mechanical properties of steel fiber reinforced concrete with different strengths of concrete

Wasim Abbass et al.

CONSTRUCTION AND BUILDING MATERIALS (2018)

Article Construction & Building Technology

Effect of super plasticizer on the properties of medium strength concrete prepared with coconut fiber

Mehran Khan et al.

CONSTRUCTION AND BUILDING MATERIALS (2018)

Review Construction & Building Technology

Different testing methods for assessing the synthetic fiber distribution in cement-based composites

Mingli Cao et al.

CONSTRUCTION AND BUILDING MATERIALS (2018)

Article Multidisciplinary Sciences

Effect of steel fibres on the compressive and flexural strength of concrete

Ashfaque Ahmed Jhatial et al.

INTERNATIONAL JOURNAL OF ADVANCED AND APPLIED SCIENCES (2018)

Article Green & Sustainable Science & Technology

Effect of class F fly ash on the durability properties of concrete

Ashish Kumer Saha

SUSTAINABLE ENVIRONMENT RESEARCH (2018)

Proceedings Paper Construction & Building Technology

Effect of Sand to Aggregate Ratio and Dosage of Admixture on High Strength Concrete Properties

Mukhlis Sunarso et al.

3RD INTERNATIONAL CONFERENCE ON CONSTRUCTION AND BUILDING ENGINEERING (ICONBUILD 2017) (2017)

Article Construction & Building Technology

Flexural capacity of fiber reinforced concrete with a consideration of concrete strength and fiber content

Jong-Han Lee et al.

CONSTRUCTION AND BUILDING MATERIALS (2017)

Article Construction & Building Technology

Flexural response of steel-fiber-reinforced concrete beams: Effects of strength, fiber content, and strain-rate

Doo-Yeol Yoo et al.

CEMENT & CONCRETE COMPOSITES (2015)

Article Construction & Building Technology

Predicting the post-cracking behavior of normal- and high-strength steel-fiber-reinforced concrete beams

Doo-Yeol Yoo et al.

CONSTRUCTION AND BUILDING MATERIALS (2015)

Article Construction & Building Technology

Mechanical and durability properties of high-strength concrete containing steel and polypropylene fibers

Vahid Afroughsabet et al.

CONSTRUCTION AND BUILDING MATERIALS (2015)

Article Construction & Building Technology

Machine learning in concrete strength simulations: Multi-nation data analytics

Jui-Sheng Chou et al.

CONSTRUCTION AND BUILDING MATERIALS (2014)

Article Construction & Building Technology

Artificial neural network for predicting drying shrinkage of concrete

Lyes Bal et al.

CONSTRUCTION AND BUILDING MATERIALS (2013)

Article Construction & Building Technology

Enhanced artificial intelligence for ensemble approach to predicting high performance concrete compressive strength

Jui-Sheng Chou et al.

CONSTRUCTION AND BUILDING MATERIALS (2013)

Article Automation & Control Systems

Two-level and hybrid ensembles of decision trees for high performance concrete compressive strength prediction

Halil Ibrahim Erdal

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE (2013)

Article Construction & Building Technology

Property assessment of steel-fibre reinforced concrete made with silica fume

Mahmoud Nili et al.

CONSTRUCTION AND BUILDING MATERIALS (2012)

Article Materials Science, Characterization & Testing

Effects of Fibre Geometry and Volume Fraction on the Flexural Behaviour of Steel-Fibre Reinforced Concrete

D. V. Soulioti et al.

STRAIN (2011)

Article Construction & Building Technology

Prediction of the compressive strength of no-slump concrete: A comparative study of regression, neural network and ANFIS models

Jafar Sobhani et al.

CONSTRUCTION AND BUILDING MATERIALS (2010)

Article Engineering, Mechanical

Combined effect of silica fume and steel fibers on the impact resistance and mechanical properties of concrete

Mahmoud Nili et al.

INTERNATIONAL JOURNAL OF IMPACT ENGINEERING (2010)

Article Computer Science, Interdisciplinary Applications

Predicting the compressive strength of mortars containing metakaolin by artificial neural networks and fuzzy logic

Mustafa Saridemir

ADVANCES IN ENGINEERING SOFTWARE (2009)

Article Construction & Building Technology

Properties of steel fiber reinforced fly ash concrete

Cengiz Duran Atis et al.

CONSTRUCTION AND BUILDING MATERIALS (2009)

Article Construction & Building Technology

Combined effect of silica fume and steel fiber on the mechanical properties of high strength concretes

Fuat Koksal et al.

CONSTRUCTION AND BUILDING MATERIALS (2008)

Article Materials Science, Multidisciplinary

Effects of super plasticizer and curing conditions on properties of concrete with and without fiber

H. Yilmaz Aruntas et al.

MATERIALS LETTERS (2008)

Article Construction & Building Technology

Mechanical properties of high strength concrete reinforced with metallic and non-metallic fibres

A. Sivakumar et al.

CEMENT & CONCRETE COMPOSITES (2007)

Article Construction & Building Technology

Mechanical properties of steel fiber-reinforced concrete

Job Thomas et al.

JOURNAL OF MATERIALS IN CIVIL ENGINEERING (2007)

Article Construction & Building Technology

Effect of aspect ratio and volume fraction of steel fiber on the mechanical properties of SFRC

Semsi Yazici et al.

CONSTRUCTION AND BUILDING MATERIALS (2007)

Article Construction & Building Technology

Predicting the compressive strength and slump of high strength concrete using neural network

Ahmet Oztas et al.

CONSTRUCTION AND BUILDING MATERIALS (2006)

Article Construction & Building Technology

Effect of reducing coarse aggregates on concrete strength

B El-Ariss

CONSTRUCTION AND BUILDING MATERIALS (2006)

Article Construction & Building Technology

Mechanical properties of high-strength steel fiber-reinforced concrete

PS Song et al.

CONSTRUCTION AND BUILDING MATERIALS (2004)

Article Construction & Building Technology

Prediction of compressive strength of concrete by neural networks

HG Ni et al.

CEMENT AND CONCRETE RESEARCH (2000)