Article
Mechanics
Seyyed-Asgar Hosseini, Mahdi Nematzadeh, Carlos Chastre
Summary: In this study, the shear performance of FRP bar-reinforced concrete beams containing steel fibers and crumb tire rubber was evaluated, and a model and empirical equation were proposed to predict their shear capacity. The proposed equation showed more accurate results compared to existing shear strength prediction equations for beams with FRP reinforcing bars.
COMPOSITE STRUCTURES
(2021)
Article
Construction & Building Technology
Haifeng Yang, Xiancheng Lu, Machi Gong, Peng Yang
Summary: Rubber powder produced from waste tires can improve the impact and shear toughness of concrete, as well as reduce environmental pollution. However, there is a lack of research on the mechanical behaviors and failure criteria of steel fiber reinforced rubber concrete (SFRRC) under combined compression-shear loading. This study investigated the effects of steel fiber and rubber powder content, and compressive stress ratio on the compression-shear properties of SFRRC. The results showed that SFRRC exhibited ductile failure under higher compressive stress ratios and the addition of steel fiber increased shear strength and peak displacement.
JOURNAL OF BUILDING ENGINEERING
(2023)
Article
Construction & Building Technology
Nolan Concha, John Rei Aratan, Eloisa Marie Derigay, Joseph Manuel Martin, Rose Erika Taneo
Summary: A hybrid Neuro-Swarm model was developed to predict the shear strength capacity of steel fiber-reinforced concrete in deep beams. The model demonstrated remarkable performance indicators and superior prediction performance.
JOURNAL OF BUILDING ENGINEERING
(2023)
Article
Engineering, Civil
Oladimeji Benedict Olalusi, Paul O. Awoyera
Summary: This study proposes the use of machine learning methods to predict the ultimate shear resistance of steel fiber reinforced concrete beams without stirrups. The ML-based models provide more accurate and unbiased predictions compared to existing design equations. Additionally, the model uncertainties were characterized and partial resistance safety factors were suggested.
ENGINEERING STRUCTURES
(2021)
Article
Construction & Building Technology
Mehran Khan, Mingli Cao, Chaopeng Xie, Majid Ali
Summary: This study examines the uniaxial compressive behavior of multi scale hybrid fiber reinforced concrete and investigates the influence of basalt fiber length and content on the properties of steel fiber-calcium carbonate whisker reinforced fly ash concrete. The results show that the multi scale hybrid fiber reinforced concrete exhibits superior compressive characteristics compared to plain concrete.
CASE STUDIES IN CONSTRUCTION MATERIALS
(2022)
Article
Engineering, Civil
Jesika Rahman, Khondaker Sakil Ahmed, Nafiz Imtiaz Khan, Kamrul Islam, Sujith Mangalathu
Summary: This study presents a data-driven approach to estimating the shear strength of steel fiber reinforced concrete beams, utilizing machine learning models. Among the 11 evaluated models, XGBoost demonstrated the most accurate predictions. Additionally, the study identified the most influential parameters affecting the shear strength of SFRC beams.
ENGINEERING STRUCTURES
(2021)
Article
Engineering, Civil
Aaron Nzambi, Denio Oliveira
Summary: This paper presents an experimental study on shear problems in steel fiber reinforced concrete beams without shear reinforcement. The specific groove in the shear span is used to locate diagonal cracks and control the critical crack inclination at 30 degrees. The results show that the proposed model is accurate in predicting shear strength, with a coefficient of variability of 4-8%.
ENGINEERING STRUCTURES
(2023)
Article
Construction & Building Technology
Mehran Khan, Mingli Cao, S. H. Chu, Majid Ali
Summary: The use of mineral basalt fibers in composites has been attracting attention due to its ecological nature. Research on the long-term behavior and mechanical properties of concrete, notably under aggressive environmental conditions, has shown significant enhancement in properties of HFRC. Mineral basalt fibers have shown promising potential in developing HFRC with improved performance under challenging conditions.
CONSTRUCTION AND BUILDING MATERIALS
(2022)
Article
Chemistry, Physical
Moiz Tariq, Azam Khan, Asad Ullah, Javad Shayanfar, Momina Niaz
Summary: In this study, a gene expression programming (GEP) model based on artificial intelligence has been developed to accurately predict the shear strength of steel fiber reinforced concrete beams. The proposed model takes into account the tensile strength of steel fibers, along with other influencing factors, and shows significant improvement compared to existing empirical models.
Article
Computer Science, Artificial Intelligence
Huan Luo, Stephanie German Paal
Summary: Existing physics-based modeling approaches have limitations in balancing performance and computational efficiency in predicting the seismic response of reinforced concrete frames. This paper proposes a novel AI-enhanced computational method that combines a shear building model with AI techniques to improve prediction accuracy while maintaining high computational efficiency.
ADVANCED ENGINEERING INFORMATICS
(2022)
Article
Construction & Building Technology
Danying Gao, Wenbin Li, Yuyang Pang, Yunchao Huang
Summary: The study demonstrates that steel fibers and stirrups can enhance the strength and ductility of SFRRAC columns, while recycled coarse aggregate has a minor impact on performance. Steel fibers improve the mechanical properties of the concrete mixture, control cover spalling, and enhance overall column behavior.
CONSTRUCTION AND BUILDING MATERIALS
(2021)
Article
Engineering, Civil
S. H. Chu, C. Unluer, D. Y. Yoo, L. Sneed, A. K. H. Kwan
Summary: The bond of steel reinforcing bars is crucial for the structural integrity of reinforced concrete, and can be improved by including fibers and using self-prestressing. This research combined self-prestressed fiber reinforced concrete (SP-FRC) and hybrid steel fiber reinforced concrete (HSFRC), resulting in bio-inspired self-prestressed hybrid steel fiber reinforced concrete (SP-HSFRC). Pull-out tests were conducted to study the bond performance of steel reinforcing bars, and analytical models were established to calculate bond parameters based on a hybrid fiber factor. A new bond model was developed for realistic bond-slip analysis, and it showed good agreement with experimental results. The improved bond was proven to effectively control cracks in HSFRC reinforced with steel bars.
ENGINEERING STRUCTURES
(2023)
Article
Chemistry, Physical
Dong Zheng, Rongxing Wu, Muhammad Sufian, Nabil Ben Kahla, Miniar Atig, Ahmed Farouk Deifalla, Oussama Accouche, Marc Azab
Summary: This study aims to predict the flexural strength of steel fiber-reinforced concrete using machine learning algorithms. The results show that the Gradient Boosting model performs the best with the highest precision and lowest error levels.
Article
Construction & Building Technology
Asmaa Said, Mahmoud Elsayed, Ahmed Abd El-Azim, Fadi Althoey, Bassam A. Tayeh
Summary: This paper evaluates the effectiveness of using ultra-high performance fiber reinforced concrete (UHPFRC) as a strengthening technique to improve the shear strength of RC beams. The experimental results show that UHPFRC is an effective technique, significantly improving the ultimate shear strength, initial stiffness, ductility, and toughness of the beams. Full casting of UHPFRC and strengthening with vertical or inclined strips were found to have a substantial contribution in increasing shear capacity.
CASE STUDIES IN CONSTRUCTION MATERIALS
(2022)
Article
Engineering, Civil
Wisena Perceka, Wen-Cheng Liao
Summary: This study aims to conduct a comprehensive experimental research on the performance of steel fiber-reinforced concrete (SFRC) columns with high-strength concrete and steel reinforcing bars under lateral displacement reversals at different axial compression ratios.
ENGINEERING STRUCTURES
(2021)
Article
Engineering, Civil
H. K. Chai, Abeer A. Majeed, Abbas A. Allawi
JOURNAL OF BRIDGE ENGINEERING
(2015)
Article
Engineering, Civil
Abeer A. Majeed, Abbas A. Allawi, Kian H. Chai, Hameedon W. Wan Badaruzzam
STRUCTURAL ENGINEERING AND MECHANICS
(2017)
Article
Multidisciplinary Sciences
Marya Bagherifaez, Arash Behnia, Abeer Aqeel Majeed, Chai Hwa Kian
SCIENTIFIC WORLD JOURNAL
(2014)
Article
Computer Science, Interdisciplinary Applications
Abeer A. Al-Musawi, Afrah A. H. Alwanas, Sinan Q. Salih, Zainab Hasan Ali, Minh Tung Tran, Zaher Mundher Yaseen
ENGINEERING WITH COMPUTERS
(2020)
Article
Engineering, Civil
Afrah Abdulelah Hamzah Alwanas, Abeer A. Al-Musawi, Sinan Q. Salih, Hai Tao, Mumtaz Ali, Zaher Mundher Yaseen
ENGINEERING STRUCTURES
(2019)
Article
Mathematics, Interdisciplinary Applications
Jamal Abdulrazzaq Khalaf, Abeer A. Majeed, Mohammed Suleman Aldlemy, Zainab Hasan Ali, Ahmed W. Al Zand, S. Adarsh, Aissa Bouaissi, Mohammed Majeed Hameed, Zaher Mundher Yaseen
Summary: Accurate prediction of PRSC shear strength is crucial in structural engineering. This study explores the use of deep learning neural network for prediction, with genetic algorithm hybridization to optimize predictor variables, achieving high accuracy with minimal parameters. The combination of DLNN and GA improves model efficiency by selecting the most significant input variables.