/library/oar/handle/123456789/16067 2026-06-23T01:30:39Z 2026-06-23T01:30:39Z Darmanin, Charlene Jo Dreyfuss, Guillaume Dalli Gonzi, Rebecca Buhagiar, Konrad /library/oar/handle/123456789/147432 2026-06-15T11:52:30Z 2026-01-01T00:00:00Z

Title: The transformation of conservation strategies in a digital era : the case for St Paul’s Anglican pro-cathedral Authors: Darmanin, Charlene Jo; Dreyfuss, Guillaume; Dalli Gonzi, Rebecca; Buhagiar, Konrad Abstract: Malta’s rich cultural heritage context hosts three UNESCO World Heritage Sites, including that of its capital, Valletta. The tower and spire at St Paul’s Anglican Pro-Cathedral (1839–1846), one of the most significant landmarks in Valletta’s skyline, has been the subject of a seven-year restoration campaign (2017–2024). This paper aims to analyse the use of digital technologies before, during and after the restoration works of this monument. A transdisciplinary approach was adopted from the early stages of the project, enabling information and knowledge to be collected from stakeholders across various disciplines, during a period of rapid transformation of digital technologies and tools. Unlike previous conservation efforts, where digital tools were often used in isolation, this study presents an integrated, transdisciplinary framework in which data collected from ground penetrating radar (GPR), UAV inspections and photogrammetry exercises, Heritage Building ¸£ÀûÔÚÏßÃâ·Ñ Modelling (HBIM) and community narratives were synthesized throughout the restoration lifecycle. The restoration campaign included for the installation of an Impressed Current Cathodic Protection (ICCP) system and a Structural Health Monitoring System, to enable the continual monitoring of the structure. Results show that the use of such technologies allowed for conservation strategies to be developed in a holistic manner, benefiting the restoration works on the tower and spire. Conclusions from this study demonstrate that digital technologies utilised throughout the lifespan of the project, in a live, decision-making environment, ensured a comprehensive approach to the restoration of built heritage, during the works and for future interventions.

2026-01-01T00:00:00Z Wang, Jiaze Huang, Yinjie Wei, Xiaoyan Zhu, Zhixuan Borg, Ruben Paul Pan, Dongyu Guo, Jiaqi Ruan, Shaoqin /library/oar/handle/123456789/147063 2026-06-02T12:33:18Z 2026-01-01T00:00:00Z

Title: Eco-mechanical synergy in low-cement CLSM from MSWIBA and TBM slurry : a Ca(OH)â‚‚-activated cross-scale engineering approach Authors: Wang, Jiaze; Huang, Yinjie; Wei, Xiaoyan; Zhu, Zhixuan; Borg, Ruben Paul; Pan, Dongyu; Guo, Jiaqi; Ruan, Shaoqin Abstract: In this study, a low-cement controlled low-strength material (CLSM) was designed by synergistically incorporating municipal solid waste incineration bottom ash (MSWIBA) and tunnel boring machine (TBM) waste slurry, with Ca(OH)â‚‚ as an activator. The roles of Ca(OH)â‚‚ in reaction pathways, multi-scale pore structure evolution, and carbon intensity were systematically investigated through rheological tests, mechanical measurements, XRD, TG/DTG, SEM-EDS, MIP, X-CT, and carbon footprint analysis. Results show that the exogenous Ca(OH)â‚‚ is completely consumed via pozzolanic reaction, clay adsorption, and early carbonation, shifting from a conventional alkaline activator to a direct reactant that governs gel chemistry while maintaining satisfactory flowability (> 180 mm). Cross-scale characterization reveals that the strength enhancement originates primarily from topological fragmentation of the defect architecture rather than from a mere reduction in total porosity. Despite a modest increase in embodied carbon due to Ca(OH)â‚‚ addition, the disproportionate strength gain reduces the carbon intensity of the CLSM by 26%. By integrating mechanistic insight, cross-scale structural engineering, and eco-mechanical assessment, this work establishes a new framework for transforming disparate solid wastes into low-carbon CLSM through rationally designed activation.

2026-01-01T00:00:00Z Prakash, Vijay Debono, Carl James Seychell, Dylan Musarat, Muhammad Ali Borg, Ruben Paul Ding, Wei Shu, Jiangpeng /library/oar/handle/123456789/146382 2026-05-12T11:58:05Z 2026-01-01T00:00:00Z

Title: DiffCNN : a neural network-based diffusion model for identification and quantification of cracks in concrete bridges Authors: Prakash, Vijay; Debono, Carl James; Seychell, Dylan; Musarat, Muhammad Ali; Borg, Ruben Paul; Ding, Wei; Shu, Jiangpeng Abstract: Concrete bridges suffer degradation and damage over time, eventually leading to failure and collapse. Therefore, monitoring these structures is necessary to identify damage early and plan timely maintenance to avoid structural failures. This paper presents a neural network-based diffusion model (DiffCNN) to identify and quantify cracks in concrete bridges' deck, wall, and pavement using both transfer learning (TL) and fully trained (FT) models. The noise control and segmentation outcome are improved with a Gaussian distribution, a CNN based architecture, and a diffusion module. Experiments on the SDNET2018 dataset show that the proposed DiffCNN model achieves a crack detection accuracy of 96.85% on the bridge deck images, 94.61% on the bridge wall images, and 99.12% on the bridge pavement images in the FT mode. Furthermore, in the TL mode, the model achieved a crack detection accuracy of 99.71% on the bridge deck images, 99.62% on the bridge wall images, and 99.93% on the bridge pavement images, outperforming traditional Deep Convolutional Neural Network (DCNN) architectures and vision transformers.

2026-01-01T00:00:00Z Musarat, Muhammad Ali Borg, Ruben Paul Wei, Jingjie Debono, Carl James Khayat, Kamal /library/oar/handle/123456789/146226 2026-05-07T12:43:12Z 2026-01-01T00:00:00Z

Title: Computer vision and machine learning approaches for defect detection in 3D-printed cementitious materials : a systematic review Authors: Musarat, Muhammad Ali; Borg, Ruben Paul; Wei, Jingjie; Debono, Carl James; Khayat, Kamal Abstract: 3D printing is evolving at a fast pace in both the manufacturing and construction sectors. These advancements can greatly benefit these industries. However, the 3D printing of concrete structures presents some challenges due to defects in the 3D concrete printed elements. Hence, this study systematically reviews Artificial Intelligence (AI)-driven techniques, such as Computer Vision and Machine Learning, to identify surface defects that can occur in 3D-printed cementitious material structures. The adopted methodology was the PRISMA statement with the aim of reporting the systematic review and meta-analysis. Two well-known databases,Web of Science and Scopus, were utilised for data extraction of articles published during the past 10 years, between 2014 and May 2025. The initial search provided 110 articles, both conference and journal papers; after screening, only 11 were left for the final review assessment. The smaller number of the final articles shows that much work is still needed in this area. It has been observed that various computer vision and machine learning-based methodologies were employed to classify defects in 3D concrete printed structures. Deep learning algorithms, such as YOLO and RT-DETR, were featured as the most efficient in real-time defect detection and quality monitoring. It was also observed that real-time monitoring systems attached to 3D printers help in reducing the material wastage, which is essential to meet the sustainable goals. However, more work is still required to underline the defects of 3D-printed cementitious material, probably with the involvement of AI image processing tools and techniques. This can help to automate the defects in 3D-printed structures, and by this, the productivity could be enhanced.

2026-01-01T00:00:00Z