Construction materials are at the forefront of global economic development as they provide the foundation for the infrastructure of other industries, with cementitious materials being predominantly used in construction projects. To promote sustainable development, alternative materials are added to cement mortar to increase durability and reduce emissions. In this regard, graphene oxide (GO) and fly ash (FA) are two alternative materials commonly used in cement mortar, which are readily available or are just the waste from other local material production. With different ratios, the amount of GO and FA can affect the properties of cement mortar positively or negatively. This study aims to determine the effects of GO and FA on cement mortar mixtures under material conditions. Research results show that 10 wt% FA and 0.036 wt% GO will give cement mortar the best physical and mechanical properties while ensuring other necessary properties, such as workability. When increasing FA to 30 wt% or GO to 0.05 wt%, the strength of the mortar mixture tends to decrease. Another issue is that the specific surface area of graphene is very high, which poses a significant challenge when uniform dispersion in the cement paste mixture is required. Polycarboxylate combined with a specific mixing sequence has demonstrated good dispersibility and high stability. Through this research, it is demonstrated that the addition of GO and FA has the potential for sustainable development of the construction industry by considering the contexts of the local recycled cementitious replacement materials.

Data loss is a recurring and critical issue in Structural Health Monitoring (SHM) systems, often arising from a range of factors including sensor malfunction, communication breakdown, and exposure to adverse environmental conditions. Such interruptions in data availability can significantly compromise the accuracy and reliability of structural performance assessments, thereby hindering effective decision-making in safety evaluation and maintenance planning. In this study, a novel deep learning-based framework is proposed for data reconstruction in SHM, employing a hybrid architecture that integrates one-dimensional convolutional neural networks (1D-CNNs) with recurrent neural networks (RNNs). By combining these complementary strengths, the hybrid 1D-CNN–RNN model demonstrates superior capacity for accurate signal reconstruction. A real-world case study was conducted using vibration data from the Trai Hut Bridge in Vietnam. Five network configurations with varying depths were examined under single- and multi-channel loss scenarios. The results confirm that the method can accurately reconstruct lost signals. For single-channel loss, the best configuration achieved an MAE = 0.019 m/s² and R² = 0.987, while for multi-channel loss, a deeper network yielded an MAE = 0.044 m/s² and R² = 0.974. Furthermore, the model exhibits robust and stable performance even under more demanding multi-channel data loss conditions, highlighting its resilience to practical operational challenges. The results demonstrate that the proposed CNN–RNN framework is accurate, robust, and adaptable for practical SHM data reconstruction applications.

Monitoring the condition of existing structures remains one of the most pressing challenges within the construction industry. Structural health monitoring (SHM) techniques have proven increasingly effective in this regard; however, maintaining and archiving complete lifecycle data for such structures remains costly. Data acquisition is particularly critical, as the SHM system relies upon this information to analyse and evaluate structural behaviour. Nonetheless, a range of challenges—such as environmental influences, sensor malfunction, and transmission failures—can lead to data corruption or loss. These issues compromise the reliability of the dataset, necessitating either data reconstruction or additional measurement campaigns, both of which are resource-intensive. This study proposes the use of a long short-term memory (LSTM) network to reconstruct missing or corrupted data. A complete dataset collected from an actual construction project is employed to train the network. Data loss scenarios are then simulated, including single-channel (loss from one sensor) and multi-channel (loss from multiple sensors) cases. The trained LSTM model is subsequently applied to reconstruct the missing data. A case study on a real bridge demonstrates that the reconstructed data show strong agreement with the original measurements in both the time and frequency domains. These findings indicate that the proposed approach has the potential to support engineers in conserving resources by reducing the need for costly and time-consuming additional measurement interventions.

Doubly curved shell roof structures have been widely studied and applied in civil buildings, because of their compressive capacity. As a spatial structure, it should increase the space, reduce the thickness of the shell, and create the architecture for the building. In particular, reinforcement is needed to repair the shell surface, forming the multilayer curved shell roof structures. In this multilayer curved shell roof, it is necessary to investigate the influence of thickness of layers, the influence of the location of the steel fibres concrete layer, and the influence of steel fibres content contained in concrete on the state of stress and strain and build relationships, load‐vertical displacement and stress in the x and y directions of the shell in the investigated cases. So, this paper presents an ANSYS numerical simulation study related to the state of stress and strain in double‐layer doubly curved concrete shell roof with the initial parameters being changed such as the thickness of the layers, the location of the steel fibres concrete layer in the structure (the steel fibres concrete layer that is placed above and below the normal concrete layer), and the steel fibres content contained in concrete shell with the size of 3000 × 3000 mm, which is simulated by ANSYS after being experimentally conducted on this curved shell roof; the results of experimental and simulation study are verified by each other. Research results show that the thickness of the steel fibres concrete layer is placed below the normal concrete layer, the percentage of steel fibres contained in the concrete is 2%, and the bearing capacity of the curved shell is optimal.

Structural health monitoring (SHM) is essential for assessing the safety and serviceability of bridge structures. Identifying progressive and concurrent damage remains challenging due to the complex and continuous nature of structural deterioration. This study proposes a hybrid one-dimensional convolutional neural network and long short-term memory (1D-CNN–LSTM) framework for vibration-based damage localisation and severity estimation in reinforced concrete bridges. Operational modal analysis is applied to field-measured vibration data from an in-service bridge. A finite element model is updated using particle swarm optimisation, reducing frequency discrepancies from 7–17% to within ±3%. Progressive single-, double-, and triple-element damage scenarios are simulated through systematic stiffness degradation. The resulting modal frequency data are used to train 1D-CNN–LSTM models using Pareto front optimisation. The proposed framework achieves coefficients of determination above 0.80 with low prediction errors (MSE and MAE < 2) for single- and double-element damage scenarios. The results support the use of the proposed framework for screening-level assessment of bridge damage under controlled simulated conditions.