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Structural Health Monitoring (SHM) system uses sensors to collect information and evaluate the structure, aiming for early damage detection. For many reasons, data from sensors can be corrupted, affecting the assessment results. Reconstructing lost or corrupted data helps complete it, improves structural assessments, and ensures structural safety. Artificial Intelligence (AI) has emerged in recent years as a solution to data problems. This study proposes the use of a One-Dimensional Convolutional Neural Network (1DCNN) to reconstruct lost vibration data during SHM. A complete dataset was used to train the 1DCNN network. After completing the training, the 1DCNN network received incomplete data to return erroneous data. The results of the study show that the proposed method is able to reconstruct vibration sensor data.

Measuring the structure's displacement plays a very important role in ensuring the safe operation of railway bridges in general and urban railway bridges in particular. In Vietnam, traditional methods using high-precision mechanical gauges have been used to measure the displacement of railway bridges. However, these methods need a lot of effort in installation and traffic control during implementation. These methods are based on the static principle: The test loads are placed on the bridge structure, and then the structure's displacement is observed. The safety assessment and analysis results are guaranteed by multiplying the dynamic coefficients, leading to some assessments that may not be close to the actual exploitation of the bridge structure. Therefore, the current study presents a new solution for measuring the displacement of railway bridge structures. This method uses Linear Variable Differential Transformer (LVDT) sensors to record the continuous displacement of the structure during the time the train passes over the bridge. Through field measurements combined with a finite element analysis model, the research focuses on developing and applying LVDT sensors in urban railway bridge structure health monitoring. At the same time, the potential of developing this method in Vietnam in the future is evaluated.

Many construction activities such as pile driving, drilling, roadbed compaction, drilling and blasting tunnels can cause ground vibration, which affects buildings and people nearby. If the wave intensity is high while the distance to neighboring buildings is close, the presence of damage in buildings is inevitable. Determination of the vibration radius which can cause damage in surrounding structures regarding each source of vibration is a necessary requirement for planning, construction technology selection and design method. It can minimize risks to surrounding buildings. The content of this article presents the measurement method to accurately estimate the vibration level of the construction activities of Hanoi pilot urban railway line 3 passing the busy Kim Ma street, Hanoi. High sensitivity vibration accelerometers are gathered in lines from the vibration source. It is shown that predicting the radius of the impact of construction site vibrations to neighboring buildings is possible. If the level of vibration is high then measures can be taken to prevent damage caused by the construction of the Hanoi pilot urban railway line. From obtained results, some solutions are proposed to minimize and avoid possible negative impacts.