Jiabin Liu

Meteorological archives often preserve abundant air-temperature records. However, verified pavement distress records are often unavailable. This makes it difficult to translate archive-scale temperature data into material thermal states that can support engineering screening and interpretation. This study develops a temperature-only probabilistic framework that links a national daily air-temperature background with asphalt and concrete thermal states through site-specific calibration. Northeast China was selected as the case study region, where synchronous 5-min observations of air, concrete, and asphalt temperatures were available from 2024 to 2025. Nationwide daily records from 1951 to 2019 place the air-temperature exposure background of the case study region in a national context. The case study region does not emerge as a dominant national hot-tail regime. Instead, it is characterized by colder minima and larger daily air-temperature ranges than the pooled national background. Under the same air-temperature exposure, asphalt showed stronger amplification of thermal peaks and diurnal cycling than concrete. In the case study region, both materials show a consistently cold-dominant screening pattern, with fluctuation screening secondary and hot screening limited. This qualitative ordering is preserved across weighting, archive-window, and transfer model sensitivity analyses, although hot and fluctuation magnitudes are less stable than the cold side estimates. The framework should therefore be interpreted as a thermal screening tool calibrated at a single monitored site, rather than as a universally validated distress or failure model.

Precise three-dimensional (3D) reconstruction of wave free surfaces and associated velocity fields is essential for developing a comprehensive understanding of ocean physics. To address the high computational cost of dense visual reconstruction in long-term ocean wave observation tasks and the challenges introduced by persistent visual occlusions, we propose an wave free surface visual reconstruction neural network, which is designed as an attention-augmented pyramid architecture tailored to the multi-scale and temporally continuous characteristics of wave motions. Using physics-based constraints, we perform time-resolved reconstruction of nonlinear 3D velocity fields from the evolving free-surface boundary. Experiments under real-sea conditions demonstrate millimetre-level wave elevation prediction in the central region, dominant-frequency errors below 0.01 Hz, precise estimation of high-frequency spectral power laws, and high-fidelity 3D reconstruction of nonlinear velocity fields, while enabling dense reconstruction of two million points in only 1.35 s. Built on a stereo-vision dataset, the model outperforms conventional visual reconstruction approaches and maintains strong generalization in occluded conditions, owing to its global multi-scale attention and its learned encoding of wave propagation dynamics.

Coastal bridges constitute critical components for the transportation in offshore areas, and thus their serviceability and safety against hazard such as earthquakes need to be ensured in a life-cycle perspective. However, coastal bridges are confronted with significant corrosion that results in degradation effects mainly on concrete piers. This can make the resistance property of bridge diverse from the initially designed state. Hence, it is vital to predict the residual performance of coastal bridges throughout its life-cycle period. Prior studies described the corrosion evolvement using mostly deterministic methods, whereas the uncertainty was neglected in terms of both the corrosion environment and concrete performance. In this study, the corrosion developed of bridge pier was investigated probabilistically. A convolutional formula was proposed to account for the correlation between the initial corrosion time and the remained time to the expected lifetime. The proposed approach was validated using a prototype bridge in China, where the corrosion environment was captured by historical chloride data, the uncertainty in other parameters was reflected using random variables. The results showed that the proposed method can well apply to predicting the corrosion state in the bridge life-cycle period, where the initial corrosion time approaches closely to skewed distribution. The selected bridge exhibited notable corrosion likelihood at the end of lifetime. It was found that the probability of corrosion absence is approximately 30% while the maximum loss mass ratio reaches around 45% for the lifespan of 100 yrs. The proposed method can be further used to determine the degraded performance for bridge analysis under other impacts such as earthquakes and waves.

Non-contact methods for measuring wave characteristics play a crucial role in wave monitoring and engineering applications, aiding in the understanding and early warning of marine environments. Over the past decade, with the rapid advancement of sensing technologies and deep learning, research on non-contact wave measurement methods has made significant progress. To gain a comprehensive understanding of the current status and challenges, this paper provides a systematic review of non-contact wave characteristic measurement methods, discussing their applications in both oceanic and laboratory settings. It introduces various effective non-contact measurement techniques, such as stereo vision, optical, and electromagnetic wave measurements, and compares and summarizes the characteristics and applicability of each method. The advantages of different sensing technologies and their applications in wave monitoring are analyzed, emphasizing the improvement in measurement accuracy and efficiency through multi-source data fusion and deep learning algorithms. The review also summarizes the strengths and limitations of deep learning-driven monocular and stereo depth measurement methods. This review aims to provide comprehensive reference and guidance for future research and applications in wave characteristic measurement technologies in both oceanic and laboratory environments.