The thermal expansion coefficient and the microscopic thermal stresses of mature concrete depend on its microstructural composition and the internal relative humidity. This dependence is determined by means of thermoelastic multiscale analysis of concrete. The underlying multiscale model enables two types of scale transition. Firstly, bottom-up homogenization allows for the quantification of the thermal expansion coefficient and the elastic stiffness of concrete based on the Mori-Tanaka scheme. Secondly, top-down scale concentration gives access to the volume averaged stresses experienced by the cement paste, the fine and the coarse aggregates and, furthermore, to the stress states of the interfacial transition zones covering the aggregates. The proposed model is validated by comparing the predicted thermal expansion coefficient of concrete with independent sets of experimental measurements. Finally, sensitivity analyses are carried out to evaluate the influence of the volumetric composition and the internal relative humidity of concrete on the thermal expansion coefficient and the microscopic thermal stresses.

Structural health monitoring (SHM) is gradually replacing traditional manual detection and is becoming a focus of the research devoted to the operation and maintenance of tunnel structures. However, in the face of massive SHM data, the autonomous early warning method is still required to further reduce the burden of manual analysis. Thus, this study proposed a dynamic warning method for SHM data based on ARIMA and applied it to the concrete strain data of the Hong Kong–Zhuhai–Macao Bridge (HZMB) immersed tunnel. First, wavelet threshold denoising was applied to filter noise from the SHM data. Then, the feasibility and accuracy of establishing an ARIMA model were verified, and it was adopted to predict future time series of SHM data. After that, an anomaly detection scheme was proposed based on the dynamic model and dynamic threshold value, which set the confidence interval of detected anomalies based on the statistical characteristics of the historical series. Finally, a hierarchical warning system was defined to classify anomalies according to their detection threshold and enable hierarchical treatments. The illustrative example of the HZMB immersed tunnel verified that a three-level (5.5 σ, 6.5 σ, and 7.5 σ) dynamic warning schematic can give good results of anomalies detection and greatly improves the efficiency of SHM data management of the tunnel.

Tunnels generally operate underground or underwater in a complex environment. As a result, the health monitoring system is inevitably affected by various environmental factors, which introduces noise to the system. However, the noise contained in the monitoring sequence may disrupt structural damage identification and health state assessment as the real structural response may be overwhelmed by the noise. To properly eliminate the noise in an objective way, this study proposed an improved wavelet threshold denoising method. Firstly, it adopts a quantitative factor, namely the Sparse Index, to assist the selection of the best wavelet basis in numerous wavelet packages. Then, the decomposition layer and threshold are optimized by a comprehensive evaluation based on a variation coefficient method. At last, the application of the concrete strain health monitoring data of the Hong Kong-Zhuhai-Macao Bridge immersed tunnel verified the effectiveness of the proposed method. It is found that the combination of sym12 and five decomposition layers can obtain the best denoising results within the selected wavelet families and decomposition levels. Moreover, the proposed method achieves good denoising results under different fluctuation levels. Thus, the proposed method is reliable, can solve the problem of optimal parameter selection such as decomposition level and wavelet basis in wavelet denoising, and can be applied in the structural health monitoring of critical infrastructures.

Very thin asphalt overlays (VTAOs) have been widely used as a cost-effective preventive maintenance measure in various countries. However, because of the complex combinations of aggregate gradations and asphalt materials, the selection of VTAOs is an unsolved problem that is extremely important for pavement management authorities. Therefore, this study proposed a comprehensive evaluation method for VTAOs based on the analytic hierarchy process (AHP) and technique for order of preference by similarity to ideal solution (TOPSIS). Three VTAO mixtures comprising different aggregate gradations (stone mastic asphalt (SMA), open-graded friction course (OGFC), and asphalt concrete (AC)) and different asphalt materials (organic silicon (OS) and styrene-butadiene-styrene (SBS)) were investigated and preliminarily compared in the laboratory. Subsequently, four road performance indicators (pavement condition indicator, British pendulum number, texture depth, and international roughness index) were selected as the evaluation indices, and their weights were calculated using the AHP according to the questionnaires collected from specialists. Finally, the field test data of the road performance indicators with scale confusion were handled using TOPSIS, and the closeness was considered as the final evaluation criterion. The results indicated that the mixture of AC and SBS exhibited the best performance among the three investigated mixtures. Categorizing the evaluation indicators into two aspects—the strength aspect and the structural aspect—it is found that the strength aspect of a VTAO is mainly affected by the asphalt materials, whereas the structural aspect of a VTAO is mainly affected by the aggregate gradation. This study provides a practical method for evaluating the road performance of VTAO with diverse measurement indices, as well as a quantitative scope for the impacts of the aggregate gradation and asphalt materials on the road performance.

Structural Health Monitoring (SHM) systems have become a crucial tool for the operational management of long tunnels. For immersed tunnels exposed to both traffic loads and the effects of the marine environment, efficiently identifying abnormal conditions from the extensive unannotated SHM data presents a significant challenge. This study proposed a model-based approach for anomaly detection and conducted validation and comparative analysis of two distinct temporal predictive models using SHM data from a real immersed tunnel. Firstly, a dynamic predictive model-based anomaly detection method is proposed, which utilizes a rolling time window for modeling to achieve dynamic prediction. Leveraging the assumption of temporal data similarity, an interval prediction value deviation was employed to determine the abnormality of the data. Subsequently, dynamic predictive models were constructed based on the Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) models. The hyperparameters of these models were optimized and selected using monitoring data from the immersed tunnel, yielding viable static and dynamic predictive models. Finally, the models were applied within the same segment of SHM data, to validate the effectiveness of the anomaly detection approach based on dynamic predictive modeling. A detailed comparative analysis discusses the discrepancies in temporal anomaly detection between the ARIMA- and LSTM-based models. The results demonstrated that the dynamic predictive model-based anomaly detection approach was effective for dealing with unannotated SHM data. In a comparison between ARIMA and LSTM, it was found that ARIMA demonstrated higher modeling efficiency, rendering it suitable for short-term predictions. In contrast, the LSTM model exhibited greater capacity to capture long-term performance trends and enhanced early warning capabilities, thereby resulting in superior overall performance.