- Performance-based seismic design of Ultra-High-Performance Concrete (UHPC) bridge columns with design example – Powered by explainable machine learning model. Dans: Engineering Structures, v. 314 (septembre 2024). (2024):
- Hybrid machine learning model and predictive equations for compressive stress-strain constitutive modelling of confined ultra-high-performance concrete (UHPC) with normal-strength steel and high-strength steel spirals. Dans: Engineering Structures, v. 304 (avril 2024). (2024):
- Towards net-zero emission: A case study investigating sustainability potential of geopolymer concrete with recycled glass powder and gold mine tailings. Dans: Journal of Building Engineering, v. 86 (juin 2024). (2024):
- A novel framework for developing environmentally sustainable and cost-effective ultra-high-performance concrete (UHPC) using advanced machine learning and multi-objective optimization techniques. Dans: Construction and Building Materials, v. 416 (février 2024). (2024):
- Explainable machine learning-aided efficient prediction model and software tool for bond strength of concrete with corroded reinforcement. Dans: Structures, v. 59 (janvier 2024). (2024):
- Development and strength prediction of sustainable concrete having binary and ternary cementitious blends and incorporating recycled aggregates from demolished UAE buildings: Experimental and machine learning-based studies. Dans: Construction and Building Materials, v. 380 (mai 2023). (2023):
- Sensitivity analysis and genetic algorithm-based shear capacity model for basalt FRC one-way slabs reinforced with BFRP bars. Dans: Composite Structures, v. 305 (février 2023). (2023):
- Explainable machine learning based efficient prediction tool for lateral cyclic response of post-tensioned base rocking steel bridge piers. Dans: Structures, v. 44 (octobre 2022). (2022):
- Repair and Retrofit of RC Bridge Piers with Steel-Reinforced Grout Jackets: An Experimental Investigation. Dans: Journal of Bridge Engineering (ASCE), v. 27, n. 8 (août 2022). (2022):
- Shear Capacity Prediction of FRP-RC Beams Using Single and Ensenble ExPlainable Machine Learning Models. Dans: Composite Structures, v. 287 (mai 2022). (2022):
- Explainable machine learning model and reliability analysis for flexural capacity prediction of RC beams strengthened in flexure with FRCM. Dans: Engineering Structures, v. 255 (mars 2022). (2022):
- Plastic hinge length of rectangular RC columns using ensemble machine learning model. Dans: Engineering Structures, v. 244 (octobre 2021). (2021):
- A shear design model for RC beams strengthened with fabric reinforced cementitious matrix. Dans: Engineering Structures, v. 200 (décembre 2019). (2019):
- Strengthening of reinforced concrete beams in shear using different steel reinforced grout techniques. Dans: Structural Concrete, v. 22, n. 2 (avril 2021). (2021):
- FRCM/internal transverse shear reinforcement interaction in shear strengthened RC beams. Dans: Composite Structures, v. 201 (octobre 2018). (2018):
- Fractional factorial design model for seismic performance of RC bridge piers retrofitted with steel-reinforced polymer composites. Dans: Engineering Structures, v. 221 (octobre 2020). (2020):
- Shear span-to-depth ratio effect on steel reinforced grout strengthened reinforced concrete beams. Dans: Engineering Structures, v. 216 (août 2020). (2020):
- Flexural strengthening of reinforced concrete beams using hybrid near-surface embedded/externally bonded fabric-reinforced cementitious matrix. Dans: Construction and Building Materials, v. 238 (mars 2020). (2020):
- Hybrid NSE/EB technique for shear strengthening of reinforced concrete beams using FRCM: Experimental study. Dans: Construction and Building Materials, v. 164 (mars 2018). (2018):