Damage detection of long-span bridges using stress influence lines incorporated control charts

Numerous long-span bridges have been built throughout the world in recent years. These bridges are progressively damaged by continuous usage throughout their long service life. The failure of local structural components is detrimental to the performance of the entire bridge, furthermore, detecting the local abnormality at an early stage is difficult. This paper explores a novel damage detection method for long-span bridges by incorporating stress influence lines (SILs) in control charts, and validates the efficacy of the method through a case study of the Tsing Ma Suspension Bridge. Damage indices based on SILs are subsequently proposed and applied to hypothetical damage scenarios in which one or two critical bridge components are subjected to severe damage. The comparison study suggests that the first-order difference of SIL change is an accurate indicator for location of the damage. To some extent, different levels of damage can be quantified by using SILs incorporating with X-bar control chart. Results of this study indicate that the proposed SIL-based method offers a promising technique for damage detection in long-span bridges. ? 2014 Science China Press and Springer-Verlag Berlin Heidelberg

Locate damage in long-span bridges based on stress influence lines and information fusion technique

To ensure bridge safety and functionality under in-service conditions, detecting local abnormalities of a long-span bridge at the early stage is always a desirable but challenging task. Stress influence lines (SIL) or its derivatives are recognized as very promising indices for damage detection. Compared with bridge global responses (such as displacement and acceleration), stress/strain can be more conveniently measured and is often more sensitive to local damages. This paper explores a novel damage localization approach by synthesizing SIL measurements from multiple locations, in which Dempster-Shafer data fusion technique is utilized. Compared with the measurement from a single sensor, more reliable damage-related information with the improved sensitivity and capability in damage localization can be obtained by synthesizing the measured SILs from a number of sensors. The effectiveness of the proposed method is validated through a numerical case study of the Tsing Ma Suspension Bridge. Different hypothetical scenarios, including single-damage case, double-damage, and no-damage cases, are considered in the validation. The comparison with the damage detection results using single-sensor data clearly indicates that the data fusion technique effectively enhance the consistency in the information (e.g., damage-induced structural change) and minimize non-consistent information (e.g. "noise" effect) from multiple sensors installed close to damage. The increasing number of sensors benefits the damage detection results. Excellent damage detection accuracy can be achieved, if different types of bridge components are properly selected for the monitoring. Therefore, it is promising to use the proposed approach in this study in the damage localization of real-world long-span bridges. Parametric studies are conducted to examine the effects of parameter selections and noise levels in this approach

Long-term outcomes of COVID-19 convalescents: An 18.5-month longitudinal study in Wuhan

Objectives: This study aimed to describe the full scope of long-term outcomes and the ongoing pathophysiological alterations among COVID-19 survivors. Methods: We established a longitudinal cohort of 208 COVID-19 convalescents and followed them at 3.3 (interquartile range [IQR]: 1.3, 4.4, visit 1), 9.2 (IQR: 9.0, 9.6, visit 2), and 18.5 (IQR: 18.2, 19.1, visit 3) months after infection, respectively. Serial changes in multiple physical and psychological outcomes were comprehensively characterized. We, in addition, explored the potential risk factors of SARS-CoV-2 antibody response and sequelae symptoms. Results: We observed continuous improvement of sequelae symptoms, lung function, chest computed tomography (CT), 6-minute walk test, and the Borg dyspnea scale, whereas sequelae symptoms (at least one) and abnormal chest CT patterns still existed in 45.2% and about 30% of participants at 18.5 months, respectively. Anxiety and depression disorders were alleviated for the convalescents, although depression status was sustained for a longer duration. Conclusions: Most COVID-19 convalescents had an overall improved physical and psychological health status, whereas sequelae symptoms, residual lesions on lung function, exercise impairment, and mental health disorders were still observed in a small proportion of participants at 18.5 months after infection. Implementing appropriate preventive and management strategies for the ever-growing COVID-19 population is warranted