https://bura.brunel.ac.uk/handle/2438/210 Fri, 16 Oct 2026 07:58:41 GMT 2026-10-16T07:58:41Z https://bura.brunel.ac.uk/handle/2438/33949 Title: An evidence-based framework for evaluating generative AI in cultural heritage reconstruction: Bamiyan, Palmyra and bounded generalisation Authors: Arzomand, Kawsar Abstract: Generative AI can transform textual, photographic, archaeological and conservation records into visual reconstruction candidates for damaged or destroyed cultural heritage. This creates an opportunity to visualise aspects of heritage appearance that were previously difficult to reconstruct from dispersed evidence alone. The evaluative problem is acute where monuments can no longer be directly recorded and reconstruction depends on incomplete photographic, archaeological and conservation evidence. Under these conditions, a generated image may appear coherent while its evidential basis, prompt construction, interpretive assumptions and cultural acceptability remain insufficiently documented. The research asks how the evaluation of GenAI reconstruction candidates can be standardised before such outputs are treated as heritage reconstruction. To address this problem, the research develops the Heritage-Aligned Reconstruction Framework (HARF) for evaluating GenAI heritage reconstruction. HARF is formalised through the Dynamic Prompt Blueprint, the Prompt Sufficiency Index, paradata, computational pre-evaluation and structured expert review. The framework is operationalised through the Western Buddha of Bamiyan, where 245 generated candidates are assessed through computational similarity, interdisciplinary screening and specialist archaeological evaluation. The Bamiyan case is extended through a community-grounded authenticity study with 62 Afghan respondents. HARF is re-instantiated at the Temple of Bel, Palmyra, to test bounded cross-site generalisation from figural sculpture to monumental architecture. The thesis makes four contributions. It establishes an evidence-constrained workflow for GenAI heritage reconstruction; formalises HARF as an integrated AI-specific evaluation framework; shows that reconstruction validity at Bamiyan is distributed across computational, specialist and community-grounded registers; and demonstrates that HARF can be transferred to Palmyra at the level of evaluative schema without redesigning its top-level structure. The Palmyra test also shows that failure domains shift with typology, from figural micro-grammar at Bamiyan to architectural and decorative micro-grammar at Palmyra. The contribution lies not in producing definitive reconstructions of Bamiyan or Palmyra, but in establishing how generated reconstruction candidates should be evaluated before they are treated as heritage reconstruction. HARF provides a structured procedure for assessing whether such candidates are evidence-based, documented, expert-assessed, culturally accountable and bounded in transfer. Description: This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University London Thu, 01 Jan 2026 00:00:00 GMT https://bura.brunel.ac.uk/handle/2438/33949 2026-01-01T00:00:00Z https://bura.brunel.ac.uk/handle/2438/33697 Title: Intelligent outlier detection under imperfect industrial data conditions Authors: Fang, Jingzhong Abstract: With the growing complexity and data intensity of modern industrial systems, intelligent data analysis (IDA) has become essential for reliable data interpretation and efficient operation. By leveraging advanced analytical and computational techniques, IDA can handle complex, largescale data sets, thereby providing valuable insights for industrial operations and significantly improving the stability and efficiency of industrial processes. Nevertheless, data quality remains a fundamental prerequisite for IDA performance, as it directly affects the accuracy and trustworthiness of derived insights. Due to complex operating conditions, high cost, and limited availability of expert annotation, high-quality data is often difficult to acquire. The imperfections in the data can affect training and evaluation, thereby reducing the overall performance in real-world applications. In practice, imperfections in industrial data generally fall into two main categories: 1) data scarcity, such as class imbalance or limited sample size; and 2) label quality issues, such as missing labels or noisy labels. In this thesis, we deal with the above-mentioned data imperfections arising from the data acquisition process and the annotation process to develop innovative approaches for robust and reliable IDA in industrial applications. It should be pointed out that all approaches developed in this thesis have been evaluated and applied to outlier detection on imperfect industrial data collected from real-world wire arc additive manufacturing (WAAM) processes. • To achieve outlier detection on unlabeled data, an improved optimization-based clustering algorithm is proposed, where the initial locations of the cluster centroids in the fuzzy C-means algorithm are optimized by an improved particle swarm optimization (PSO) algorithm. An adaptive switching randomly perturbed particle swarm optimization (ASRPPSO) algorithm is developed to enhance the convergence rate and particle’s search ability of the PSO algorithm, where a distance-based weighting strategy and switching strategy are introduced to update parameters of the optimizer. The particles can conduct a thorough search by accounting for both evolutionary states and the distances to the global and personal best positions, thereby improving the convergence rate and solution accuracy. Via the ASRPPSO-based selection of optimal clustering centroids, the proposed algorithm does not rely on centroid initialization, thereby facilitating a better cluster partition. • With the aim to guarantee the outlier detection performance on imbalanced data under the small sample problem, an optimized deep transfer learning framework is developed, where a novel deep domain adaptation strategy is designed to minimize the cross-domain discrepancies, the weighting factors are designed to handle the data imbalance problem, and the PSO algorithm is utilized for hyper-parameters tuning. By leveraging the domain knowledge and optimal hyper-parameter selection, the developed framework effectively balances performance and efficiency when dealing with outlier detection tasks on imbalanced data under the small sample problem. • To handle the noisy label problem under limited-data conditions in outlier detection, a novel Transformer-embedded learning with noisy labels framework with fuzzyclustering- assisted contrastive learning (TFCCL) is developed, where a fuzzy-clusteringassisted contrastive learning approach, a dynamic two-stage training scheme and a joint learning strategy are introduced to train the outlier detector. The TFCCL framework integrates supervised learning with contrastive learning, thereby reducing reliance on potentially noisy labels and enhancing model robustness. • For the purpose of outlier detection under the noisy label problem when sufficient data are available, a role-differentiated learning with noisy labels (RD-LNL) approach is put forward, where a leader-follower-inspired sample selection (LFSS) strategy is proposed for identifying potential clean samples for model training. A selection metric and an adaptive selection scheme are designed to adjust the number of selected samples. By combining the joint learning and adaptive sample selection, the RD-LNL framework achieves robust outlier detection in the presence of noisy labels. • To fully evaluate the application potential of the developed frameworks. All the developed frameworks are applied to outlier detection tasks on WAAM data sets collected from real-world manufacturing processes to examine their adaptability, robustness, and generalization capability. The experimental results demonstrate the effective and robust performance of the developed outlier detection frameworks on WAAM data sets across various data imperfection conditions. Description: This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University London Thu, 01 Jan 2026 00:00:00 GMT https://bura.brunel.ac.uk/handle/2438/33697 2026-01-01T00:00:00Z https://bura.brunel.ac.uk/handle/2438/33501 Title: Smart cities in Qatar toward a sustainable digital transformation Authors: Majareh, Maryam Abstract: The rapid progression of digital transformation has reshaped urban landscapes globally, fostering the development of smart cities. This thesis investigates the sustainability of digital transformation within the context of smart cities, focusing specifically on Qatar. With its ambitious national vision and substantial investments in smart city technologies, Qatar is a critical case study for understanding how digital transformation can be implemented to drive long-term sustainability. The research delves into critical areas such as governance frameworks, technological infrastructure, data management, and citizen engagement, all crucial for building resilient and sustainable smart cities. It also explores how digital solutions contribute to environmental, social, and economic sustainability, highlighting the role of emerging technologies like AI, IoT, and blockchain in improving city operations, resource management, and quality of life. The study uses quantitative and qualitative data from a survey and interviews with expert stakeholders to evaluate digital transformation efforts in Qatar's smart cities, including Lusail and Msheireb Downtown. It assesses their alignment with sustainability goals and recommends fostering an inclusive, efficient, and future-proof digital infrastructure. It also identifies challenges related to cybersecurity, regulatory frameworks, and digital inclusion, offering insights on how these can be mitigated to ensure the sustainability of Qatar's smart city initiatives. This research identifies key lessons applicable to other developing smart cities through evaluating Qatar’s approach, including the requirement of a well-defined regulatory framework, which can help in maintaining a balance between innovation and security, the significance of public-private partnerships in increasing technological adoption, and the role of digital literacy programs in ensuring inclusive participation. The thematic map and PRISMA synthesis together show that long-term sustainability depends not only on infrastructure investment, but also on governance capacity, interoperability, stakeholder trust, and adaptive regulation. This research contributes to the broader discourse on smart cities by presenting a comprehensive framework for sustainable digital transformation that is supported by survey analysis, stakeholder interviews, thematic mapping, and PRISMA-based evidence synthesis. It offers valuable lessons for Qatar's policymakers, technologists, urban planners, and others. Description: This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University London Wed, 01 Jan 2025 00:00:00 GMT https://bura.brunel.ac.uk/handle/2438/33501 2025-01-01T00:00:00Z https://bura.brunel.ac.uk/handle/2438/33430 Title: Understanding the search space: Investigations into the nature of software modularisation Authors: Mann, Ashley J. Abstract: A relationship exists between the functionality of software systems and their complex-ity. As the number of features implemented increases, the systems complexity also grows, accompanied by the expansion of the number of artefacts and their intricate in-terrelationships [1]. Description: This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University London Thu, 01 Jan 2026 00:00:00 GMT https://bura.brunel.ac.uk/handle/2438/33430 2026-01-01T00:00:00Z