Editor


Tadesse Gemeda Wakjira Qatar University, Qatar
Email: twakjira@qu.edu.qa

Tel.: +97450442229


Tadesse Gemeda Wakjira

[1] Sholi HY Al, Wakjira T, Kutty AA, Habib S, Alfadhli M, Aejas B, et al. Circular economy towards achieving carbon-neutral and sustainable mega sporting events: Lessons learned from FIFA World Cup Qatar 2022 (In press). Circ Econ 2023.

[2] Al-Hamrani A, Wakjira TG, Alnahhal W, Ebead U. Sensitivity analysis and genetic algorithm-based shear capacity model for basalt FRC one-way slabs reinforced with BFRP bars. Compos Struct 2023;305. https://doi.org/10.1016/j.compstruct.2022.116473.

[3] AlKhereibi AH, Wakjira TG, Kucukvar M, Onat NC. Predictive Machine Learning Algorithms for Metro Ridership Based on Urban Land Use Policies in Support of Transit-Oriented Development. Sustainability 2023;15:1718. https://doi.org/10.3390/su15021718.

[4] Kutty AA, Wakjira TG, Kucukvar M, Abdella GM, Onat NC. Urban resilience and livability performance of European smart cities: A novel machine learning approach. J Clean Prod 2022;378. https://doi.org/10.1016/j.jclepro.2022.134203.

[5] Wakjira TG, Abushanab A, Ebead U, Alnahhal W. FAI: Fast, accurate, and intelligent approach and prediction tool for flexural capacity of FRP-RC beams based on super-learner machine learning model. Mater Today Commun 2022;33. https://doi.org/10.1016/j.mtcomm.2022.104461.

[6] Wakjira TG, Rahmzadeh A, Alam MS, Tremblay R. Explainable machine learning based efficient prediction tool for lateral cyclic response of post-tensioned base rocking steel bridge piers. Structures 2022;44:947–64. https://doi.org/10.1016/j.istruc.2022.08.023.

[7] Kennedy-Kuiper RCS, Wakjira TG, Alam MS. Repair and Retrofit of RC Bridge Piers with Steel-Reinforced Grout Jackets: An Experimental Investigation. J Bridg Eng 2022;27. https://doi.org/https://doi.org/10.1061/(asce)be.1943-5592.0001903.

[8] Wakjira TG, Ebead U, Alam MS. Machine learning-based shear capacity prediction and reliability analysis of shear-critical RC beams strengthened with inorganic composites. Case Stud Constr Mater 2022;16:e01008. https://doi.org/10.1016/j.cscm.2022.e01008.

[9] Wakjira TG, Al-Hamrani A, Ebead U, Alnahhal W. Shear capacity prediction of FRP-RC beams using single and ensenble ExPlainable Machine learning models. Compos Struct 2022;287. https://doi.org/10.1016/j.compstruct.2022.115381.

[10] Wakjira TG, Ibrahim M, Ebead U, Alam MS. Explainable machine learning model and reliability analysis for flexural capacity prediction of RC beams strengthened in flexure with FRCM. Eng Struct 2022;255. https://doi.org/10.1016/j.engstruct.2022.113903.

[11] Wakjira TG, Alam MS, Ebead U. Plastic hinge length of rectangular RC columns using ensemble machine learning model. Eng Struct 2021;244:112808. https://doi.org/10.1016/j.engstruct.2021.112808.

[12] Wakjira TG, Ebead U. Strengthening of reinforced concrete beams in shear using different steel reinforced grout techniques. Struct Concr 2021;22:1113–27. https://doi.org/10.1002/suco.202000354.

[13] Wakjira TG, Nehdi ML, Ebead U. Fractional factorial design model for seismic performance of RC bridge piers retrofitted with steel-reinforced polymer composites. Eng Struct 2020;221:111100. https://doi.org/10.1016/j.engstruct.2020.111100.

[14] Wakjira TG, Ebead U. Shear span-to-depth ratio effect on steel reinforced grout strengthened reinforced concrete beams. Eng Struct 2020;216:110737. https://doi.org/10.1016/j.engstruct.2020.110737.

[15] Wakjira TG, Ebead U. Simplified Compression Field Theory-Based Model for Shear Strength of Fabric-Reinforced Cementitious Matrix- Strengthened Reinforced Concrete Beams. ACI Struct J 2020;117:91–104. https://doi.org/10.14359/51721366.

[16] Ibrahim M, Wakjira TG, Ebead U. Shear strengthening of reinforced concrete deep beams using near-surface mounted hybrid carbon/glass fibre reinforced polymer strips. Eng Struct 2020;210:110412. https://doi.org/https://doi.org/10.1016/j.engstruct.2020.110412.

[17] El-Sherif HE, Wakjira TG, Ebead U. Flexural strengthening of reinforced concrete beams using hybrid near-surface embedded/externally bonded fabric-reinforced cementitious matrix. Constr Build Mater 2020;238:117748. https://doi.org/https://doi.org/10.1016/j.conbuildmat.2019.117748.

[18] Wakjira TG, Ebead U. A shear design model for RC beams strengthened with fabric reinforced cementitious matrix. Eng Struct 2019;200:109698. https://doi.org/https://doi.org/10.1016/j.engstruct.2019.109698.

[19] Wakjira TG, Ebead U, Alam MS. Machine Learning-Based Shear Capacity Prediction and Reliability Analysis of RC Beams Strengthened with Inorganic Composites. Case Stud Constr Mater 2022.

[20] Wakjira TG, Al-Hamrani A, Ebead U, Alnahhal W. Shear capacity prediction of FRP-RC beams using single and ensenble ExPlainable machine learning models. Compos Struct 2022;287:115381. https://doi.org/10.1016/j.compstruct.2022.115381.

[21] Timmermans C, Alhajyaseen W, Al Mamun A, Wakjira TG, Qasem M, Almallah M, et al. Analysis of road traffic crashes in the State of Qatar. Int J Inj Contr Saf Promot 2019;26:242–50. https://doi.org/https://doi.org/10.1080/17457300.2019.1620289.

[22] Wakjira TG, Ebead U. FRCM/internal transverse shear reinforcement interaction in shear strengthened RC beams. Compos Struct 2018;201:326–39. https://doi.org/https://doi.org/10.1016/j.compstruct.2018.06.034.

[23] Wakjira TG, Ebead U. Hybrid NSE/EB technique for shear strengthening of reinforced concrete beams using FRCM: Experimental study. Constr Build Mater 2018;164:164–77. https://doi.org/https://doi.org/10.1016/j.conbuildmat.2017.12.224.