PSI - Issue 70

Abdul Musavvir et al. / Procedia Structural Integrity 70 (2025) 432–439

439

Ali Elheber Ahmed Elshekh;Nasir Shafiq;Muhd Fadhil Nuruddin;Ahmed Fathi , 2013. Mechanical Properties of High Strength Concrete Using Fly Ash. IEEE Business Engineering and Industrial Applications Colloquium. Er. Tushant Seth, Dr Sandeep Singla, 2023. A Review on Concrete Strength Prediction Models Based On Machine Learning Algorithms. International Journal of Innovative Research in Engineering & Management. H N Muliauwan, D Prayogo, G Gaby and K Harsono, 2020. Prediction of Concrete Compressive Strength Using Artificial Intelligence Methods. Journal of Physics: Conference Series. Huu-Bang Tran, Vu To-Anh Phan, 2024. Potential Usage of Fly Ash and Nano Silica in High-Strength Concrete: Laboratory Experiment and Application in Rigid Pavement. Case Studies in Construction Materials. Ibrahim Y.Hakeem,MohammadAlharthai,MohamedAmin,Abdullah M.Zeyad,Bassam A.Tayeh,Ibrahim SaadAgwa, 2023. Properties of sustainable high-strength concrete containing large quantities of industrial wastes, nanosilica and recycled aggregates. Journal of Materials Research and Technology. I.-C. Yeh, 1998. Modeling of strength of high-performance concrete using artificial neural networks. Cement and Concrete Research. John F. Vargas, Ana I. Oviedo, Estebana Orozco,Ana Gómez, Jorge M. Londoño, 2024. Machine-Learning-Based Predictive Models for Compressive Strength, Flexural Strength, and Slump of Concrete. Applied Sciences. Manish Kumar, Rahul Biswas, Divesh Ranjan Kumar, Pijush Samui, Mosbeh R. Kaloop, Mohamed Eldessouki, 2023. Soft Computing-Based Prediction Models for Compressive Strength of Concrete. Case Studies in Construction Materials Mary Devika Bandaru, Suseela Kyle, Tallapudi Indira Priyadarshini, Durga Vara Prasad Bokka & Ungarala Ganesh Sai, 2024. Predicting the Compressive Strength of Concrete by Using Machine Learning Techniques. Journal of Physics: Conference Series Meltem Özturan, Birgül Kutlu, Turan Özturan, 2008. Comparison of Concrete Strength Prediction Techniques with Artificial Neural Network Approach. Building Research Journal. Mohammad Mohtasham,Moein, Ashkan Saradar, Komeil Rahmati, Seyedhosein Ghasemzadehmousavinejad, James Bristow, Vartenie Aramali, MosesKarakouzian, 2023. Predictive Models for Concrete Properties Using Machine Learning and Deep Learning Approaches: A Review. Journal of Building Engineering. NaraindasBheel,AhsanWaqar,DorinRadu,OmraneBenjeddou,MamdoohAlwetaishi,Hamad R.Almujibah, 2024. A comprehensive study on the impact of nano-silica and ground granulated blast furnace slag on high strength concrete characteristics: RSM modeling and optimization. Structures. Prashant M. Dhamanage, Mysore V. Nagendra, 2020. High Strength Concrete: A Review. International Research Journal of Engineering and Technology. (Priyanka Singh, 2023) Priscila Silva,Gray Farias Moita,Vanderci F Arruda, 2020. Machine Learning Techniques to Predict the Compressive Strength of Concrete. RIMNI. Priyanka Singh, Ng Cheng Yee, and Bashar S. Mohammed. "Utilizing Stearic-Acid-Coated Marble Dust for the Production of Eco-Friendly Self Cleaning Concrete: RSM Modeling and Optimization." Sustainability 15, no. 11 (2023): 8635. Priyanka Singh, Garg Chakshu, Aman Namdeo, Abhishek Singhal, Rabindra Nath Shaw, and Ankush Ghosh. "Adaptive fuzzy logic models for the prediction of compressive strength of sustainable concrete." In Advanced Computing and Intelligent Technologies: Proceedings of ICACIT 2021, pp. 593-605. Springer Singapore, 2022. Sajjad,KamranGoshtasbi,Hamid RezaNejati,JavadKarimi,Arsham MoayediFar, 2025. Investigating the fatigue performance of Nano-Silica modified concrete with various admixtures: An experimental study. Results in Engineering. Sana Shabir Khan, Anuj Sachar, Sandeep Singla, 2022. Predicting the Concrete Properties Using Machine Learning- A Step Towards Smart Infrastructure. International Journal of Innovative Research in Computer Science & Technology. Sara Elhishi, Asmaa Mohammed Elashry, Sara El-Metwally, 2023. Unboxing Machine Learning Models for Concrete Strength Prediction Using XAI. Scientific Reports. Shashikant Kumar, Rakesh Kumar, Baboo Rai, Pijush Samui, 2024. Prediction of Compressive Strength of High-Volume Fly Ash Self-Compacting Concrete with Silica Fume Using Machine Learning Techniques. Construction and Building Materials. Singh, P., Adebanjo, A., Shafiq, N., Razak, S. N. A., Kumar, V., Farhan, S. A., ... & Sergeevna, M. T. 2024. Development of performance-based models for green concrete using multiple linear regression and artificial neuralnetwork.International Journal on Interactive Design and Manufacturing (IJIDeM), 18(5), 2945-2956. Suhang Yang, Jingsong Sun, Xu Zhifeng, 2024. Prediction on Compressive Strength of Recycled Aggregate Self-Compacting Concrete by Machine Learning Method. Journal of Building Engineering. Torkan Shafighfard, Farzin Kazemi, Neda Asgarkhani, Doo-Yeol Yoo, 2024. Machine-Learning Methods for Estimating Compressive Strength of High-Performance Alkali-Activated Concrete. Engineering Applications of Artificial Intelligence. V.V. Praveen Kumar, Subhashish Dey, 2023. Study on Strength and Durability Characteristics of Nano-Silica Based Blended Concrete. Hybrid Advances. Vimal Rathakrishnan, Salmia Bt. Beddu & Ali Najahahmed, 2022. Predicting Compressive Strength of High Performance Concrete with High Volume Ground Granulated Blast Furnace Slag Replacement Using Boosting Machine Learning Algorithms. Scientifc Reports. Yingqing Lyu, Haijun Wu, Heng Dong, Guang Ren, Tongqing Jia, Fenglei Huang, 2024. Dynamic Failure Characteristics of High-Strength Concrete and High-Strength Rock Based on Fractal Theory. Engineering Fracture Mechanics. Yansheng Liu, Ruyan Li, Qian Liu, Zhen Tian, 2024. Differential based integrated model for predicting concrete slumps. Engineering Science and Technology an International Journal.