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Word cloudA word cloud is a visual representation of the most frequently used words in a text or a set of texts. The words appear in different sizes, with the size of each word being proportional to its frequency of occurrence in the text. The more frequently a word is used, the larger it appears in the word cloud. This technique allows for a quick visualization of the most important themes and concepts in a text.
In the context of this page, the word cloud was generated from the publications of the author {}. The words in this cloud come from the titles, abstracts, and keywords of the author's articles and research papers. By analyzing this word cloud, you can get an overview of the most recurring and significant topics and research areas in the author's work.
The word cloud is a useful tool for identifying trends and main themes in a corpus of texts, thus facilitating the understanding and analysis of content in a visual and intuitive way.
Roy, D., Das, D., Islam, K., & Billah, A. H. M. M. (2025). Machine learning assisted axial strength prediction models for concrete filled stainless steel tubular columns. Structures, 73, 108329 (18 pages). External link
Rahman, J., Billah, A. H. M. M., Arafin, P., Islam, K., & Nehdi, M. L. (2024). Design-focused Interpretable Machine Learning Models for Compressive Capacity Prediction of Gusset Plate Connections. Engineering Structures, 298, 117038 (13 pages). External link
Cakiroglu, C., Tusher, T. H., Shahjalal, M., Islam, K., Billah, A. H. M. M., & Nehdi, M. L. (2024). Explainable ensemble learning graphical user interface for predicting rebar bond strength and failure mode in recycled coarse aggregate concrete. Developments in the Built Environment, 20, 100547 (25 pages). External link
Cakiroglu, C., Shahjalal, M., Islam, K., Mahmood, S. M. F., Billah, A. H. M. M., & Nehdi, M. L. (2023). Explainable ensemble learning data-driven modeling of mechanical properties of fiber-reinforced rubberized recycled aggregate concrete. Journal of Building Engineering, 76, 22 pages. External link
Gholipour, G., & Billah, A. H. M. M. (2023). Numerical investigation on the dynamic behavior of UHPFRC strengthened rocking concrete bridge piers subjected to vehicle collision. Engineering Structures, 288, 116241 (22 pages). External link
Karim, M. R., Islam, K., Billah, A. H. M. M., & Alam, M. S. (2023). Shear Strength Prediction of Slender Concrete Beams Reinforced with FRP Rebar Using Data-Driven Machine Learning Algorithms. Journal of Composites for Construction, 27(2), 18 pages. External link
Shahjalal, M., Islam, K., Rahman, J., Ahmed, K. S., Karim, M. R., & Billah, A. H. M. M. (2021). Flexural response of fiber reinforced concrete beams with waste tires rubber and recycled aggregate. Journal of Cleaner Production, 278, 123842 (18 pages). External link
Islam, K., Billah, A. H. M. M., Chowdhury, M. M. I., & Ahmed, K. S. (2020). Exploratory study on bond behavior of plain and sand coated stainless steel rebars in concrete. Structures, 27, 2365-2378. External link
Nahar, M., Islam, K., & Billah, A. H. M. M. (2020). Seismic collapse safety assessment of concrete beam-column joints reinforced with different types of shape memory alloy rebars. Journal of Building Engineering, 29, 14 pages. External link
