In 2023, natural catastrophes caused overall losses of US$ 250bn worldwide, with earthquakes accounting for an overall economic loss of US$ 50bn and 58,000 fatalities (Munic RE, 2024). Typically, the collapse of buildings contributes to over 95% of the total casualties (Xu et al., 2019). After an earthquake occurs, timely community recovery will depend on the capacity to ensure that buildings in the affected region are safe to reoccupy. The seismic damage assessment of reinforced concrete (RC) buildings is crucial for post-earthquake building appraisal, particularly for critical facilities used for disaster shelters. Normally, inspectors examine buildings for indications of damage affecting their vertical or lateral load-bearing systems, as well as potential non-structural hazards. The extent of earthquake-induced damage to RC structures is usually quantified using expert knowledge and manual inspections. Also, assembling a proficient inspection team will take several weeks, and carrying out the inspections will require many months. Inspectors will encounter hazardous circumstances in the aftermath of disasters. Ultimately, inspections will be based on personal interpretation; inspectors will often have limited availability to construction records and must depend on their intuition on-site. Recently, computer tools have been used to automatically detect and evaluate structural cracks in building components with the intent to improve safety, speed, and consistency of evaluations to inform decision-makers on the suitability of structures for occupancy. Nevertheless, the mere identification of cracks on the building's exterior will prove inadequate; in order to evaluate the building's overall safety, the damage must be confined to specific components and analysed in relation to the structural system's function of that component.

The purpose of this PhD is to construct a comprehensive digital twin framework that facilitates swift evaluations of building safety in the aftermath of earthquakes. This framework will incorporate Building Information Modelling (BIM) and Computer Vision (CV) damage detection. With the objective of safeguarding the well-being and recovery of the community in the aftermath of an earthquake, this framework attempts to rectify the urgent requirement for precise and expeditious evaluation of seismic damage to reinforced concrete (RC) structures, specifically those functioning as emergency shelters. The primary goal is to use existing databases to collect data and advance algorithms to identify damage and analyse structures in a way that current human inspection methods can't. Key deliverables consist of:

1. Establishing a Structural Analysis Framework: Integrating FEMA P-58, FEMA P-154, and FEMA P-2055 evaluation frameworks with existing collected image data to offer a comprehensive model which can assess a building's state.
2. Develop Dataset: Review existing datasets and develop damage classes to train and deploy a computer vision model.
3. Enhancing Precision and Uniformity: Formulating techniques to precisely identify cracks and evaluate damage in relation to particular building components and their functions within the structural system, thus surpassing simple identification of cracks.
4. Creating Digital Twins: Using BIM to produce digital copies of damaged structures that will be used to conduct structural examination and damage categorisation.
5. Facilitating Decision-Making: Offer a dependable evaluation instrument that can apprise decision-makers of the occupancy suitability and structural soundness of buildings in the aftermath of an earthquake, thereby contributing to the overall efficacy of disaster response and management.

The key novelty of the proposed project is as follows:

With the use of BIM, this research proposes creating a digital copy or replica of buildings, capturing their current state and geometry through techniques like laser scanning, photogrammetry, or UAV to develop a digital twin framework for rapid post-earthquake building safety assessments. This PhD project aims to integrate damage detection through Computer Vision and BIM for post-earthquake building evaluation and develop a digital twin framework.

Deep learning architecture such as VGG19, ResNet50, and Inception V3 represents the cutting edge of crack detection for infrastructure projects. VGG19 is limited by training time and model size – due to model weight, ResNet50 can suffer from vanishing gradient and overfitting- especially when training on a small dataset, while Inception V3 are complex, computationally costly and can be challenging to interpret and fine-tune (Nijaguna et al., 2023). Unfortunately, choosing, refining, and deploying a model require expert knowledge, and existing models are contradictory in outcome due to sub-optimum architectural, weighting, and training - calling for simplified deep learning models. Automating processes like feature engineering, model selection, and hyperparameter tuning, automated machine learning, or AutoML is proposed for the first time for deployment in developing machine learning models for structural damage identification.

BIM facilitates the limitations of connecting local and global computer vision within a structural health monitoring context. This framework will link collected picture data to an existing structure that will be analysed. A survey will take pictures of a damaged structure after an earthquake and access existing open-source databases (SDNET2018, SDNET2021, CODEBRIM, CONCORNET2023, and BiNet, etc.) containing damaged structures and components. Each of the discussed datasets is different in characteristics such as pixel, quality, colour, etc., and can be pre-processed and combined into a unified format to obtain a more useful dataset to develop and train a computer vision model which classifies damage to components and systems. Repurposing this dataset represents one of the contributions of this study. Each image will be superimposed on the BIM to link any damage that is found to particular building components. These components will then be categorised into discrete damage states as per FEMA P-59, P-154 and P-2055. Non-structural damage states will also be classified.

The digital representation of a building can provide invaluable information for analysing and understanding the structural health and if the building can be used post-earthquake. The framework will go beyond the understanding of individual building components and consider how these components interact and the risk to the overall system. System twins are valuable for analysing system behaviour. The outcomes of this study are expected to provide a useful tool for the rapid seismic damage assessment of buildings and assist the contingency response and management.

Arafin, P., Billah, A.M. and Issa, A., 2024. Deep learning-based concrete defects classification and detection using semantic segmentation. Structural Health Monitoring, 23(1), pp.383-409.
Deng, J., Singh, A., Zhou, Y., Lu, Y. and Lee , V.C.S., 2022. Review on computer vision-based crack detection and quantification methodologies for civil structures. Construction and Building Materials, 356, p.129238.
Dong, C.Z. and Catbas, F.N., 2021. A review of computer vision–based structural health monitoring at local and global levels. Structural Health Monitoring, 20(2), pp.692-743.
Federal Emergency Management Agency (FEMA) 2018. FEMA P-58, Development of Next Generation Performance-Based Seismic Design Procedures for New and Existing Buildings
Federal Emergency Management Agency (FEMA) 2015. FEMA P-154, Rapid Visual Screening of Buildings for Potential Seismic Hazards: A Handbook
Federal Emergency Management Agency (FEMA) 2019. FEMA P-2055, Post-disaster Safety Assessments
Kumar, A., Martin, H. and Leon, L., 2023, June. Concrete damage identification for structural health monitoring using computer vision. In 11th International Conference on Fiber-Reinforced Polymer (FRP) Composites in Civil Engineering (CICE 2023) (p. 125). Zenodo.
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Munic Re (2024) Nat cat loss events 2023: Natural catastrophes caused overall losses of US$ 250bn worldwide. Available at: https://reliefweb.int/map/world/nat-cat-loss-events-2023-natural-catastrophes-caused-overall-losses-us-250bn-worldwide. Cited May 17, 2024
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ESSENTIAL BACKGROUND OF CANDIDATES

The candidate should have a minimum of a strong upper second class (2.1) honours degree (completed or in the final stages of completion) in structural engineering, building information modelling (BIM), computer science, or other related engineering disciplines. The candidate must be competent in either MATLAB, Python, C++, or other language. Knowledge of GIS or LiDAR remote sensing is not necessary, but it would be an asset.

RESEARCH PROPOSAL - INFORMATION FOR APPLICANTS

Please note that applicants are not required to upload a research proposal as part of the application. Instead, interested candidates should upload a copy of their CV and a covering letter outlining their motivation to undertake a PhD on this theme, and describing any relevant experience in civil or structural engineering, building information modelling (BIM), computer science, or other related engineering disciplines.

APPLICATION PROCEDURE

• Apply for Degree of Doctor of Philosophy in Civil Engineering at Queen's University Belfast, School of Natural and Built Environment.
• State name of lead supervisor on the application form' Dr Hector Martin'.
• Include your Research Proposal (see above for research proposal guidance).
• State the intended SOURCE OF FUNDING on your application as 'EPSRC'
• To apply, visit https://dap.qub.ac.uk/portal/user/u_login.php (link to the QUB Direct Application Portal)

Funding Information

PLEASE NOTE: These EPSRC studentships are open only to candidates who are classed Home, UK or Republic of Ireland and candidates with settled status or ILTR. International candidates are not eligible. The value of an award includes the cost of approved fees as well as maintenance support (stipend). As an indicator, the level for 2023/2024 is currently £18,622.

Please note that this research project is one of several advertised projects at Queen’s which are in competition for funding. The selection will be based on the projects which receive the best application.