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As the construction of long-span bridges such as cable-stayed bridges increases worldwide, maintaining bridge serviceability and operability has become an important issue in civil engineering. The stay cable is a principal component of cable-stayed bridges and is generally lightly damped and intrinsically vulnerable to vibration. Excessive vibrations in stay cables can potentially cause long-term fatigue accumulation and serviceability issues. Previous studies have mainly focused on the mitigation of cable vibration within an acceptable operational level, while little attention has been paid to the quantitative assessment of serviceability enhancement provided by vibration control. This study accordingly proposed and evaluated a serviceability assessment method for stay cables equipped with vibration control. Cable serviceability failure was defined according to the range of acceptable cable responses provided in most bridge design codes. The cable serviceability failure probability was then determined by means of the first-passage problem using VanMarcke’s approximation. The proposed approach effectively allows the probability of serviceability failure to be calculated depending on the properties of any installed vibration control method. To demonstrate the proposed method, the stay cables of the Second Jindo Bridge in South Korea were evaluated and the analysis results accurately reflected cable behavior during a known wind event and show that the appropriate selection of vibration control method and properties can effectively reduce the probability of serviceability failure.

Socially and economically marginalized people and environmentally vulnerable areas are disproportionately affected by natural hazards. Identifying populations and places vulnerable to disasters is important for disaster management, and crucial for mitigating their economic consequences. From the fields of geography, emergency management, and urban planning, several approaches and methodologies have been used to identify significant vulnerability factors affecting the incidence and impact of disasters. This study performs a regression analysis to examine several factors associated with disaster damage in 230 local communities in South Korea, using ten vulnerability indicators for social, economic, and environmental aspects, and a single indicator for disaster characteristics. A Lagrange Multiplier diagnostic test-based spatial autoregressive model (SAM) was applied to assess the potential spatial autocorrelation in the ordinary least squares (OLS) residuals. This study compared the OLS regression results with those of a spatial autoregressive model, for both presence of spatial autocorrelation, and model performance. The conclusion of this study is that Korean communities with a higher vulnerability to disasters, as a result of their socioeconomic and environmental characteristics, are more likely to experience economic losses from natural disasters.

The most important structural element of prestressed concrete (PSC) bridges is the prestressed tendon, and in order to ensure safety of such bridges, it is very important to determine whether the tendon is damaged. However, it is not easy to detect tendon damage in real time. This study proposes a novelty detection approach for damage to the tendons of PSC bridges based on a convolutional autoencoder (CAE). The proposed method employs simulation data from nine accelerometers. The accuracies of CAEs for multi-vehicle are 79.5%–85.8% for 100% and 75% damage severities with all error levels and 50% damage severity without error. However, the accuracies for 50% damage severity with 5% and 10% error levels drop to 69.4%–73.3%. The accuracies of CAEs for single-vehicle ranges from 90.1%–95.1% for all damage severities and error levels that are satisfactory. The findings indicate that the CAE approach for multi-vehicle can be effective when the damages are severe, but not when moderate. Meanwhile, if acceleration data can be obtained for single-vehicle, then the CAE approach can provide a highly accurate and robust method of tendon damage detection in PSC bridges in use, even if the measurement errors are significant.

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As demand for long-span bridges is increasing worldwide, effective maintenance has become a critical issue to maintain their structural integrity and prolong their lifetime. Given that a stay-cable is the principal load-carrying component in cable-stayed bridges, monitoring tension forces in stay-cables provides critical data regarding the structural condition of bridges. Indeed, various methodologies have been proposed to measure cable tension forces, including the magneto-elastic effect-based sensor technique, direct measurement using load cells, and indirect tension estimation based on cable vibration. In particular, vibration-based tension estimation has been widely applied to systems for tension monitoring and is known as a cost-effective approach. However, full automation under different cable tension forces has not been reported in the literature thus far. This study proposes an automated cable tension monitoring system using deep learning and wireless smart sensors that enables tension forces to be estimated. A fully automated peak-picking algorithm tailored to cable vibration is developed using a region-based convolution neural network to apply the vibration-based tension estimation method to automated cable tension monitoring. The developed system features embedded processing on wireless smart sensors, which includes data acquisition, power spectral density calculation, peak-picking, post-processing for peak-selection, and tension estimation. A series of laboratory and field tests are conducted on a cable to validate the performance of the proposed automated monitoring system.