Staff profile

Short Bio

Dr Imrose Muhit is an Assistant Professor of Civil and Structural Engineering in the Department of Engineering. His research focuses on sustainable and climate-resilient infrastructure, with expertise in construction materials, structural reliability, infrastructure resilience, and digital engineering for infrastructure asset management.

He obtained his PhD in Civil Engineering from the University of Newcastle (UON, Australia) in 2021, where he investigated the reliability and performance of masonry buildings under wind hazards. He subsequently worked as a Postdoctoral Researcher at UON before joining the University of Leeds (UK) as a Postdoctoral Research Fellow, contributing to the EPSRC-funded ERMABI project on the long-term behaviour and resilience of ageing masonry arch bridges.

Prior to joining Durham University, Dr Muhit was a Lecturer and subsequently Senior Lecturer at Teesside University (UK), where he led and contributed to multiple externally funded research projects spanning low-carbon construction materials and resilient infrastructure.

Dr Muhit is a Fellow of the Higher Education Academy (FHEA), a Senior Member of RILEM (The International Union of Laboratories and Experts in Construction Materials, Systems and Structures), and serves on the Community Advisory Board of the Institution of Civil Engineers (ICE), contributing to discussions on professional practice, community engagement, and the future skills needs of the civil engineering profession. He is also a member of the UK Young Academy (2024 Cohort), which brings together outstanding early-career leaders from across the UK to address pressing national and global challenges.

Research Interests

Dr Muhit's research focuses on the development and assessment of sustainable and climate-resilient infrastructure, with particular emphasis on the ageing, degradation, durability, and long-term performance of construction materials and infrastructure systems. His work spans materials, structures, and digital technologies to improve resilience, reliability, and whole-life performance.

His research interests include:

• Durability, ageing, and degradation of concrete, masonry, low-carbon, and bio-based construction materials.
• Structural reliability and performance-based design of materials and infrastructure systems.
• Infrastructure performance and resilience under climate change and multi-hazard scenarios.
• Artificial Intelligence (AI), data-driven methods, and predictive Digital Twins for sustainable infrastructure management.

PhD and Postdoctoral Opportunities

Dr Muhit welcomes enquiries from highly motivated UK and international students interested in pursuing PhD research in his areas of expertise. Prospective applicants with external scholarship funding, sponsorship from their home country, or other sources of funding are encouraged to get in touch with a CV and a brief outline of their research interests.

He also welcomes expressions of interest from postdoctoral researchers seeking to develop competitive fellowship applications. He is keen to support outstanding candidates applying for prestigious schemes such as the Marie Skłodowska-Curie Postdoctoral Fellowships, Leverhulme Early Career Fellowships, Newton International Fellowships, and other relevant national and international funding programmes.

Funded Research Projects

[1] Knowledge Transfer Partnerships (KTP) with Poundfield Precast Limited; Innovate UK. Principal Investigator (PI); 09/2026 - 03/2029; Value: £283,000. Ref: 10189936

[2] MECRETE - Advancing Mechano-Chemical Recycling Technologies for Valorisation of Mineral Wastes; EPSRC UKRI. Co-Investigator (Co-I); 04/2026 - 03/2029; Value: £1,300,000. Ref: APP83410

[3] Knowledge Transfer Partnerships (KTP) with Dauson Environmental Group; Innovate UK. Co-Investigator (Co-I); 03/2025 - 09/2027; Value: £263,000. Ref: 10140234

[4] Making Waves - Ocean literacy across UK secondary school subjects; The Royal Society (through UKYA). Co-Investigator (Co-I); 01/2025 - 12/2025; Value: £10,000.

[5] Activated Filter Cake - Scaling Up Brick Binding Technology to Reduce Landfill Waste; Innovate UK. Co-Investigator (Co-I); 03/2023 - 03/2025; Value: £1,000,000. Ref: 10059263

PhD Supervision

Current PhD Students

• Mr Wisdom Asotah - Development of Low-Carbon Concrete Using Construction Demolition Waste, Tunnel Excavated Material, and Slag. Year 3, Teesside University (Primary Supervisor)
• Mr Partho Sharma - Concrete Durability Enhancement through Re-Alkalization, Crack Self-Healing, and Repair. Year 1, Teesside University (Co-Supervisor)

Completed PhD Students

• Dr Delbaz Samadian - A Novel Framework for Measuring Multi-Hazard Vulnerability of Steel Buildings Using Machine Learning. April 2026, Teesside University (Primary Supervisor)

Journal Article

• Numerical modelling of masonry arch bridges: Classical strategies and emerging approaches
  
  Grillanda, N., Grosman, S., Niero, L., He, L., & Muhit, I. B. (2026). Numerical modelling of masonry arch bridges: Classical strategies and emerging approaches. Engineering Structures, 360, Article 122665. https://doi.org/10.1016/j.engstruct.2026.122665
• Introducing energy-based seismic and flood demand (EBSFD) for multi-hazard vulnerability analysis of buildings
  
  Samadian, D., Eslamnia, H., Muhit, I. B., & Dawood, N. (2026). Introducing energy-based seismic and flood demand (EBSFD) for multi-hazard vulnerability analysis of buildings. Journal of Building Engineering, 128, Article 116558. https://doi.org/10.1016/j.jobe.2026.116558
• An interpretable machine learning approach for shear capacity prediction of prestressed UHPC bridge girders with GUI deployment
  
  Jaiswal, R., Bhatta, N., & Muhit, I. B. (2026). An interpretable machine learning approach for shear capacity prediction of prestressed UHPC bridge girders with GUI deployment. Structures, 88, Article 111793. https://doi.org/10.1016/j.istruc.2026.111793
• Stack-AttenLSTM: A surrogate deep learning model for sequential earthquake-flood structural response assessment of steel buildings
  
  Samadian, D., Occhipinti, A., Muhit, I. B., & Dawood, N. (2026). Stack-AttenLSTM: A surrogate deep learning model for sequential earthquake-flood structural response assessment of steel buildings. Engineering Structures, 353, Article 122345. https://doi.org/10.1016/j.engstruct.2026.122345
• Progress in Computational Modelling for Concrete Durability and Its Integration with Artificial Intelligence and Life-Cycle Assessment
  
  Muhit, I. B., Al-Fakih, A., Suntharalingam, T., & Michel, A. (2026). Progress in Computational Modelling for Concrete Durability and Its Integration with Artificial Intelligence and Life-Cycle Assessment. Archives of Computational Methods in Engineering, 33(2), 2751-2783. https://doi.org/10.1007/s11831-025-10373-x
• Surrogate-Based Resilience Assessment of SMRF Buildings Under Sequential Earthquake–Flood Hazards
  
  Samadian, D., & Muhit, I. B. (2026). Surrogate-Based Resilience Assessment of SMRF Buildings Under Sequential Earthquake–Flood Hazards. Buildings, 16(1), Article 48. https://doi.org/10.3390/buildings16010048
• Comprehensive sustainability assessment of Ferrock: innovations for the sustainable built environment
  
  Muhit, I. B., Al-Fakih, A., & Mbiu, R. N. (2025). Comprehensive sustainability assessment of Ferrock: innovations for the sustainable built environment. Smart and Sustainable Built Environment, 14(7), 2201-2234. https://doi.org/10.1108/sasbe-06-2024-0222
• Semi-probabilistic failure analysis of masonry veneer walls under lateral loading using a Discrete Macro-Element Model
  
  Hindya, D., Muhit, I. B., & Panto, B. (2025). Semi-probabilistic failure analysis of masonry veneer walls under lateral loading using a Discrete Macro-Element Model. Engineering Failure Analysis, 182(Part A), Article 109973. https://doi.org/10.1016/j.engfailanal.2025.109973
• An integrated framework for 3D time history analysis of steel special moment-resisting frame buildings under sequential flood and earthquake hazards
  
  An integrated framework for 3D time history analysis of steel special moment-resisting frame buildings under sequential flood and earthquake hazards. (2025). Structure and Infrastructure Engineering. Advance online publication. https://doi.org/10.1080/15732479.2025.2591815
• Optimization of compressive strength and carbon footprint in fly ash geopolymer concrete using metaheuristic algorithms
  
  Al-Fakih, A., Saleh, R. A. A., Muhit, I. B., & Al-Wajih, E. (2025). Optimization of compressive strength and carbon footprint in fly ash geopolymer concrete using metaheuristic algorithms. Environment, Development and Sustainability. Advance online publication. https://doi.org/10.1007/s10668-025-06957-z
• Shear capacity prediction and reliability analysis of corroded reinforced concrete beams using deep generative modeling and ensemble learning
  
  Simwanda, L., David, A., Olalusi, O., Muhit, I., & Sykora, M. (2025). Shear capacity prediction and reliability analysis of corroded reinforced concrete beams using deep generative modeling and ensemble learning. Engineering Applications of Artificial Intelligence, 157, Article 111085. https://doi.org/10.1016/j.engappai.2025.111085
• Integrating modern bioeconomy into macroeconomics: A comprehensive review of impacts and interactions
  
  Pashakolaie, V. G., Gonella, S., & Muhit, I. B. (2025). Integrating modern bioeconomy into macroeconomics: A comprehensive review of impacts and interactions. Bioresource Technology Reports, 30, Article 102125. https://doi.org/10.1016/j.biteb.2025.102125
• Reliability-based durability requirements for RC structures made of low-carbon concretes in climate change conditions
  
  Val, D. V., Malami, S. I., Suryanto, B., & Muhit, I. B. (2025). Reliability-based durability requirements for RC structures made of low-carbon concretes in climate change conditions. Civil Engineering and Environmental Systems, 42(2), 164-189. https://doi.org/10.1080/10286608.2025.2478008
• Application of Data-Driven Surrogate Models in Structural Engineering: A Literature Review
  
  Samadian, D., Muhit, I. B., & Dawood, N. (2025). Application of Data-Driven Surrogate Models in Structural Engineering: A Literature Review. Archives of Computational Methods in Engineering, 32(2), 735-784. https://doi.org/10.1007/s11831-024-10152-0
• Optimizing modified asphalt binder performance at high and intermediate temperatures using experimental and machine learning approaches
  
  Riyad, R. H., Jaiswal, R., Muhit, I. B., & Shen, J. (2024). Optimizing modified asphalt binder performance at high and intermediate temperatures using experimental and machine learning approaches. Construction and Building Materials, 449, Article 138350. https://doi.org/10.1016/j.conbuildmat.2024.138350
• Structural failure analysis with CMS-based ground motion selection using innovative cost function and weight factors
  
  Samadian, D., Fayaz, J., Muhit, I. B., & Dawood, N. (2024). Structural failure analysis with CMS-based ground motion selection using innovative cost function and weight factors. Earthquake Engineering and Engineering Vibration, 23(4), 899-918. https://doi.org/10.1007/s11803-024-2279-z
• Ensemble machine learning models for predicting the CO2 footprint of GGBFS-based geopolymer concrete
  
  Al-Fakih, A., Al-wajih, E., Saleh, R. A., & Muhit, I. B. (2024). Ensemble machine learning models for predicting the CO2 footprint of GGBFS-based geopolymer concrete. Journal of Cleaner Production, 472, Article 143463. https://doi.org/10.1016/j.jclepro.2024.143463
• Systematic review of experimental testing of masonry walls’ failure: Comparative analysis and future directions
  
  Dauda, J. A., Iuorio, O., Muhit, I. B., & da Silva, L. C. (2024). Systematic review of experimental testing of masonry walls’ failure: Comparative analysis and future directions. Engineering Failure Analysis, 163(B), Article 108571. https://doi.org/10.1016/j.engfailanal.2024.108571
• Surrogate models for seismic and pushover response prediction of steel special moment resisting frames
  
  Samadian, D., Muhit, I. B., Occhipinti, A., & Dawood, N. (2024). Surrogate models for seismic and pushover response prediction of steel special moment resisting frames. Engineering Structures, 314, Article 118307. https://doi.org/10.1016/j.engstruct.2024.118307
• A holistic sustainability overview of hemp as building and highway construction materials
  
  Muhit, I. B., Omairey, E. L., & Pashakolaie, V. G. (2024). A holistic sustainability overview of hemp as building and highway construction materials. Building and Environment, 256, Article 111470. https://doi.org/10.1016/j.buildenv.2024.111470
• Meta databases of steel frame buildings for surrogate modelling and machine learning-based feature importance analysis
  
  Samadian, D., Fayaz, J., Muhit, I. B., Occhipinti, A., & Dawood, N. (2024). Meta databases of steel frame buildings for surrogate modelling and machine learning-based feature importance analysis. Resilient Cities and Structures, 3(1), 20-43. https://doi.org/10.1016/j.rcns.2023.12.001
• Evaluation of self-compacting rubberized concrete properties: Experimental and machine learning approach
  
  Ofuyatan, O. M., Muhit, I. B., Babafemi, A. J., & Osibanjo, I. (2023). Evaluation of self-compacting rubberized concrete properties: Experimental and machine learning approach. Structures, 58, Article 105423. https://doi.org/10.1016/j.istruc.2023.105423
• Failure analysis and structural reliability of unreinforced masonry veneer walls: Influence of wall tie corrosion
  
  Muhit, I. B., Masia, M. J., & Stewart, M. G. (2023). Failure analysis and structural reliability of unreinforced masonry veneer walls: Influence of wall tie corrosion. Engineering Failure Analysis, 151, Article 107354. https://doi.org/10.1016/j.engfailanal.2023.107354
• Spatial variability and stochastic finite element model of unreinforced masonry veneer wall system under Out-of-plane loading
  
  Muhit, I. B., Masia, M. J., Stewart, M. G., & Isfeld, A. C. (2022). Spatial variability and stochastic finite element model of unreinforced masonry veneer wall system under Out-of-plane loading. Engineering Structures, 267, Article 114674. https://doi.org/10.1016/j.engstruct.2022.114674
• Probabilistic constitutive law for masonry veneer wall ties
  
  Muhit, I. B., Stewart, M. G., & Masia, M. J. (2022). Probabilistic constitutive law for masonry veneer wall ties. Australian Journal of Structural Engineering, 23(2), 97-118. https://doi.org/10.1080/13287982.2021.2021628
• Monte-Carlo laboratory testing of unreinforced masonry veneer wall system under out-of-plane loading
  
  Muhit, I. B., Masia, M. J., & Stewart, M. G. (2022). Monte-Carlo laboratory testing of unreinforced masonry veneer wall system under out-of-plane loading. Construction and Building Materials, 321, Article 126334. https://doi.org/10.1016/j.conbuildmat.2022.126334
• Structural shear retrofitting of reinforced concrete beam: multilayer ferrocement technique
  
  Muhit, I. B., Jitu, N., & Alam, M. R. (2021). Structural shear retrofitting of reinforced concrete beam: multilayer ferrocement technique. Asian Journal of Civil Engineering, 22(2), 191-203. https://doi.org/10.1007/s42107-020-00306-3
• Effect of Strain Rate on Impact Behavior of Aluminum Foam
  
  Muhit, I. B., Shim, C. S., Yun, N. R., & Park, S. D. (2019). Effect of Strain Rate on Impact Behavior of Aluminum Foam. KSCE Journal of Civil Engineering, 23(11), 4852-4863. https://doi.org/10.1007/s12205-019-5827-8
• Determination of mortar strength using stone dust as a partially replaced material for cement and sand
  
  Muhit, I. B., Raihan, M. T., & Nuruzzaman, M. (2014). Determination of mortar strength using stone dust as a partially replaced material for cement and sand. Advances in Concrete Construction, 2(4), 249-259. https://doi.org/10.12989/acc.2014.2.4.249
• Influence of Crushed Coarse Aggregates on Properties of Concrete
  
  Muhit, I. B., Haque, S., & Rabiul Alam, M. (2013). Influence of Crushed Coarse Aggregates on Properties of Concrete. American Journal of Civil Engineering and Architecture, 1(5), 103-106. https://doi.org/10.12691/ajcea-1-5-3