Search

The inter-story drift ratio (IDR) is a crucial parameter in structural health monitoring to judge the safety, stability, and serviceability of buildings. Real-time, continuous, and widely applicable detection on IDRs is essential. However, the current methods present some challenges in conducting such detection, including adverse effects from weather, the requirement for large amounts of space, and fragile instruments. This study proposes an alternative method to overcome these defects to measure IDRs and evaluate the structural conditions using ambient noise recordings. Ambient noise is a random and continuous wave signal with various sources and is modified by its propagating medium. Taking the Zhonghe Building on the campus of Tongji University, Shanghai, China, as an example, 24 three-component seismometers were deployed to capture and record the ambient noise continuously from 20 November 2021 to 9 December 2021. Using analysis of the polarization parameters of ambient noise during the building's most dangerous time and ordinary time, a deflection curve, IDRs, and harmful IDRs of the Zhonghe Building during the most dangerous time were calculated. The computed maximum drift was 0.06 m, the maximum IDR was 1.140 × 10 − 3 , and the maximum harmful IDR was − 2.573 × 10 − 4 . These results were compared with the relevant specifications in China, and it was found that the structure was in good condition. The study proposes an alternative method to measure IDRs with high applicability and continuity in real time and underscores the need for further research to achieve a localized and real-time structural health monitoring system. [ABSTRACT FROM AUTHOR]

In recent years, natural disasters happening over the world pose severe challenges to emergency evacuation systems, especially for highly-populated urban community areas. Underground spaces not systematically utilized yet provide a new development direction for the construction of emergency shelters. Emergency response for mitigating negative impacts of disasters calls for well-designed shelter system and effective management of pedestrian evacuation. In this paper, we study the location selection of underground emergency shelters in urban community areas, along with pedestrian evacuation planning once disaster happens. The aim is to effectively use the existing underground space and sidewalk network to satisfy pedestrian evacuation demand as much as possible. We formulate the problem as a network flow model, and develop a minimum-cost-maximum-flow solution approach. A real-world case study based on the East Nanjing Road community area in central Shanghai is conducted. The results show that sidewalks are the bottleneck of the pedestrian evacuation network due to their limited capacity. Reserving vehicle lanes, implementing one-way evacuation, and opening local small-scale underground shelters, are shown to be effective measures for improving the pedestrian evacuation performance. Overall, the proposed methodology and managerial insights based on the case study in Shanghai show a great potential of employing underground space as shelters for pedestrian evacuation system design in urban community areas. [ABSTRACT FROM AUTHOR]

As a sustainable transportation alternative, cycling has been developed by many large cities as one essential measure for addressing the "last mile problem" in urban areas with high mobility demand. Developing a safe and friendly lane network for cycling has become an urgent task for governments, especially for many Chinese cities encountering a rapid increase in the use of "dockless" shared bikes, such as Mobike and Ofo. The emergence of such kind of app-driven dockless bike sharing systems results in a fast growth in the cycling mobility demand, and leads to a gap between the growing demand and the existing cycling infrastructure. Therefore, it is imperative to have a good understanding of the cycling travel demand and its infrastructure, and in turn to bridge the supply-demand gap. In this paper, we employ data mining techniques including graphic clustering algorithm, Louvain Method, on a large data set from one of the largest bike sharing companies in China. Then we applied the methodology in two cases in Shanghai, including a campus and an urban area. Typical cycling patterns in the spatial and temporal dimensions are identified automatically by the cluster analysis. It is also found that the construction of the cycling infrastructure is closely related to three factors: non-negligible impact of points of interest (POIs), geographical barriers, and temporal variance of the network. Managerial insights and policy measures are proposed accordingly for improving the cycle lane network. [ABSTRACT FROM AUTHOR]

The current monitoring, reporting, and verification (MRV) and offset (O) systems usually require at least one or two months, which is time-consuming. We proposed a highly efficient real-time MRV + O mechanism based on Internet of Things (IoT) and blockchain technology. This mechanism includes the accounting model for a certain distributed carbon reduction scenario, real-time M system, real-time RV system, and real-time O system, enabling individuals to reliably declare their distributed carbon abatement behaviors in real-time. We modified the existing calculating methodology for carbon reduction published by the Clean Development Mechanism (CDM) and included factors such as air conditioning systems etc., in the selected distributed carbon reduction scenario named battery electric vehicles (BEVs)/hybrid electric vehicles (HEVs) driving. In our case study in Shanghai, we analyzed the hourly carbon dioxide emissions reductions based on approximately 20,000 driving records of four different kinds of BEVs/HEVs using our real-time MRV + O framework to verify the effectiveness of our system. We found that the proposed system can achieve hourly analysis and avoid falsified values; BEVs and HEVs could reduce greenhouse emissions by 64.54% and 46.42%, respectively. As public engagement is essential to achieve carbon neutralization in a country, this system can accelerate the sustainable transformation by delicacy management of distributed carbon abatement behaviors. [Display omitted] • A novel real-time MRV + O mechanism based on Iot and blockchain technology for the distributed carbon abatement behaviors. • We modified carbon reduction calculation model of battery electric vehicles (BEVs)/hybrid electric vehicles (HEVs) driving. • A case study with nearly 20,000 observations in Shanghai, China demonstrated its high efficiency and reliability. • A win-win carbon ecosystem involved government, business and individuals to achieve the delicacy management. [ABSTRACT FROM AUTHOR]