https://www.um.edu.mt/library/oar/handle/123456789/16068 2026-06-10T19:30:31Z 2026-06-10T19:30:31Z Wang, Jiaze Huang, Yinjie Wei, Xiaoyan Zhu, Zhixuan Borg, Ruben Paul Pan, Dongyu Guo, Jiaqi Ruan, Shaoqin https://www.um.edu.mt/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)₂-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)₂ as an activator. The roles of Ca(OH)₂ 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)₂ 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)₂ 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 https://www.um.edu.mt/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 https://www.um.edu.mt/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 Zhang, Derun Tang, Jinbiao Luan, Dongxing Xu, Xiong Borg, Ruben Paul Lewis, Odette https://www.um.edu.mt/library/oar/handle/123456789/145406 2026-04-08T11:56:20Z 2026-01-01T00:00:00Z

Title: Waste tire rubber recycling for developing a high viscosity-elasticity composite modified asphalt Authors: Zhang, Derun; Tang, Jinbiao; Luan, Dongxing; Xu, Xiong; Borg, Ruben Paul; Lewis, Odette Abstract: Non-biodegradable waste tire rubber poses serious environmental and public health risks. Thus, waste tires recycling has become a critical global issue. In this study, crumb rubber (CR) derived from waste tires was recycled to synthesize a new type of high viscosity-elasticity composite modified asphalt (RSTMA) with styrene-butadiene block copolymer (SBS), and terpene resin (T105). Three major indicators, dynamic viscosity, elastic recovery rate, and softening point difference after segregation were used to determine the optimal formula of RSTMA. Dynamic shear rheometer (DSR) tests were employed to systematically evaluate the rheological properties of RSTMA. The individual contributions of the three modifiers to RSTMA performance improvement were quantified via the Entropy Weight Method (EWM). Experimental results show that compared with base asphalt, the elastic recovery rate of RSTMA with the optimal formulation increases by 71%, while the non-recoverable creep compliance decreases by 98.56%. The segregation index (SI) of this optimal RSTMA reaches 0.87, and its fatigue life at a 35% damage degree exceeds 8000 loading cycles. It was also found that CR significantly increases the viscosity upper limit of RSTMA, CR and T105 jointly elevate its elasticity upper limit, while SBS improves both viscosity and elasticity. Overall, under the synergistic modification of CR-SBS-T105, the performance of RSTMA is significantly enhanced, with CR content reaching up to 15% (by asphalt mass). This study provides a pathway for the high-value, low-carbon recycling of crumb rubber, promoting sustainable development in the solid waste management.

2026-01-01T00:00:00Z