Seismic loss assessment of RC high-rise buildings designed according to Eurocode 8

A probabilistic seismic loss assessment of RC high-rise(RCHR)buildings designed according to Eurocode 8 and located in the Southern Euro-Mediterranean zone is presented herein.The loss assessment methodology is based on a comprehensive simulation approach which takes into account ground motion(GM)uncertainty,and the random effects in seismic demand,as well as in predicting the damage states(DSs).The methodology is implemented on three RCHR buildings of 20-story,30-story and 40-story with a core wall structural system.The loss functions described by a cumulative lognormal probability distribution are obtained for two intensity levels for a large set of simulations(NLTHAs)based on 60 GM records with a wide range of magnitude(M),distance to source(R)and different site soil conditions(SS).The losses expressed in percent of building replacement cost for RCHR buildings are obtained.In the estimation of losses,both structural(S)and nonstructural(NS)damage for four DSs are considered.The effect of different GM characteristics(M,R and SS)on the obtained losses are investigated.Finally,the estimated performance of the RCHR buildings are checked to ensure that they fulfill limit state requirements according to Eurocode 8.

Population and housing grid spatialization in Yunnan Province based on grid sampling and application of rapid earthquake loss assessment:the Jinggu Ms6. 6 earthquake

Population and housing grid data spatialization hased on 340 grid samples （ 1 kmx 1 kin） is used in- stead of regional statistical data to simulate the population and housing distribution data of Yunnan Province （ 1 km×1 kin） for rapid loss assessment ibr the Jinggu Ms6.6 earthquake. The resuhs indicate that the method reflects the actual population and housing distribution and that the assessment results are eredihle. The method can be used to quickly provide spatial orientation disaster information after an earthquake.

The housing loss assessment of rural villages caused by earthquake disaster in Yunnan Province

Based on the assessment report of destructive housings caused by more than 20 earthquakes occurred in Yunnan Province in 1990~2004, the vulnerability models of 4 types housings of rural residents in Yunnan Province are setup. The scenario earthquake disaster loss model is used to simulate the housing loss if the historical earthquakes that occurred since A.D. 886 in Yunnan Province reoccur in 2002. The analyses show the simulation deviation of the usual earthquakes is less than 30% and the method is of high practicality. Meanwhile, the simulation result of 398 historical earthquakes in Yunnan Province shows that the annual economic loss caused by the earthquakes is about RMB 410 million Yuan that accounts for 0.18% of GDP of Yunnan Province for the year. Because the per capita living area and the price of the housing increases year by year, if the historical destructive earthquake reoc- curs today, the loss of Yunnan Province will be greater than in those years.

Construction of an Index System for Earthquake Emergency Loss Assessment and Decision-making

Loss assessment and decision-making are essential for earthquake emergency rescues,and for scientific prediction of seismic damage and determination of rescue objectives.In practice,however,there exist some problems,such as basic data not being precise and rich enough and decision making not having systematic and complete criteria.This paper tries to solve these problems using the method of data indexation by constructing an index system for earthquake emergency loss assessment and decision-making.

Fast assessment of earthquake loss and its application to the 2008 M_S8.0 Wenchuan earthquake

The paper introduces firstly the seismic loss assessment method based on c indicators and new vulnerability models determined by the data from the on-site damage and loss survey to earthquakes occurred in China during the last two decades. The fast assessment for the 2008 Wenchuan earthquake with Ms8.0 is given based on an empirical intensity attenuation relationship. Compared with the assessment based on the practical seismic intensity map of the event according to the on-site investigation, the result demonstrates the usability of the seismic vulnerability models introduced in the paper. In addition, it is indicated that the main uncertainty of losses in the fast loss assessment comes from the uncertainty of the estimation of seismic ground motion.

GIS-based Effect Assessment of Soil Erosion Before and After Gully Land Consolidation: A Case Study of Wangjiagou Project Region, Loess Plateau

The Loess Plateau is one typical area of serious soil erosion in the world. China has implemented ′Grain for Green′(GFG) project to restore the eco-environment of the Loess Plateau since 1999. With the GFG project subsidy approaching the end, it is concerned that farmers of fewer subsidies may reclaim land again. Thus, ′Gully Land Consolidation Project′(GLCP) was initiated in 2010. The core of the GLCP was to create more land suitable for farming in gullies so as to reduce land reclamation on the slopes which are ecological vulnerable areas. This paper aims to assess the effect of the GLCP on soil erosion problems by studying Wangjiagou project region located in the central part of Anzi valley in the middle of the Loess Plateau, mainly using the revised universal soil loss equation(RUSLE) based on GIS. The findings show that the GLCP can help to reduce soil shipment by 9.87% and it creates more terraces and river-nearby land suitable for farming which account for 27.41% of the whole study area. Thus, it is feasible to implement the GLCP in places below gradient 15°, though the GLCP also intensifies soil erosion in certain places such as field ridge, village land, floodplain, natural grassland, and shrub land. In short, the GLCP develops new generation dam land and balances the short-term and long-term interests to ease the conflicts between economic development and environmental protection. Furthermore, the GLCP and the GFG could also be combined preferably. On the one hand, the GFG improves the ecological environment, which could offer certain safety to the GLCP, on the other hand, the GLCP creates more farmland favorable for farming in gullies instead of land reclamation on the slopes, which could indirectly protect the GFG project.

Mismatch loss analysis of solar array output power on stratospheric airship during flight

Stratospheric airships are long-endurance aerostats and have broad applications.All of the energy required for their operation is obtained from solar radiation,which makes accurate calculation of the energy output from the solar array crucial to the design and flight planning of the airships.However,the status of each photovoltaic module in the solar array may differ due to the airship curvature,resulting in mismatch losses and lowered output power,which has not been widely studied.In this paper,an irradiation model and a thermal model are established based on the actual arrangement of the modules.The output power model is established considering the non-uniform radiation in the array.The mismatch losses of the array are analyzed under different flight conditions.The output power of the solar array is decreased by up to 31.6%compared to the ideal state.Moreover,the proportion of mismatch losses increases with latitude,but the maximum mismatch loss power occurs at mid-latitudes.Then,an array reconfiguration method is proposed based on the irradiance dispersion index and position dispersion index.The reconfigured array increases output power by 11.5%and can maintain energy balance in continuous flight.The results can be used to correct the overestimation of the output power during the airship design or to guide the configuration of the solar array.

A review of the research and application of deep learning-based computer vision in structural damage detection

Damage detection is a key procedure in maintenance throughout structures′life cycles and post-disaster loss assessment.Due to the complex types of structural damages and the low efficiency and safety of manual detection,detecting damages with high efficiency and accuracy is the most popular research direction in civil engineering.Computer vision(CV)technology and deep learning(DL)algorithms are considered as promising tools to address the aforementioned challenges.The paper aims to systematically summarized the research and applications of DL-based CV technology in the field of damage detection in recent years.The basic concepts of DL-based CV technology are introduced first.The implementation steps of creating a damage detection dataset and some typical datasets are reviewed.CV-based structural damage detection algorithms are divided into three categories,namely,image classification-based(IC-based)algorithms,object detection-based(OD-based)algorithms,and semantic segmentation-based(SS-based)algorithms.Finally,the problems to be solved and future research directions are discussed.The foundation for promoting the deep integration of DL-based CV technology in structural damage detection and structural seismic damage identification has been laid.

The Advances and Prospects of Research on Earthquake Engineering at IEM

This paper describes briefly the recent advances and achievements of the research projects conducted by the Institute of Engineering Mechanics (IEM) in the period of the Ninth Five-Year Plan (1995～2000) with the support of the China Seismological Bureau (CSB). These projects are related with key problems in the field of earthquake engineering. They are: development of the methods for determining earthquake resistant design load level, study on mechanisms of earthquake damage to buildings; development of new technology of base isolation, and study on earthquake damage prediction and seismic loss assessment methods. Through these studies, quite a number of problems have been solved and some of them have been applied in earthquake engineering design and practice.

Using Climate Factors to Estimate Flood Economic Loss Risk

Estimation of economic loss is essential for stakeholders to manage flood risk.Most flooding events are closely related to extreme precipitation,which is influenced by large-scale climate factors.Considering the lagged influence of climate factors,we developed a flood-risk assessment framework and used Hunan Province in China as an example to illustrate the risk assessment process.The main patterns of precipitation—as a connection between climate factors and flood economic losses—were extracted by the empirical orthogonal function(EOF)analysis.We identified the correlative climate factors through crosscorrelation analysis and established a multiple stepwise linear regression model to forecast future precipitation patterns.Risk assessment was done based on the main precipitation patterns.Because the economic dataset is limited,a Monte Carlo simulation was applied to simulate 1000-year flood loss events under each precipitation regime(rainy,dry,normal years)to obtain aggregate exceedance probability(AEP)and occurrence exceedance probability(OEP)curves.We found that precipitation has a strong influence on economic loss risk,with the highest risk in rainy years.Regional economic development imbalances are the potential reason for the varying economic loss risks in different regions of Hunan Province.As the climate indices with at least several months prediction lead time are strong indicators in predicting precipitation,the framework we developed can estimate economic loss risk several months in advance.

Modeling the Adverse Impact of Rainstorms on a Regional Transport Network

Cities are centers of socioeconomic activities,and transport networks carry cargoes and passengers from one city to another. However, transport networks are influenced by meteorological hazards, such as rainstorms,hurricanes, and fog. Adverse weather impacts can easily spread over a network. Existing models evaluating such impacts usually neglect the transdisciplinary nature of approaches for dealing with this problem. In this article, a mesoscopic mathematical model is proposed to quantitatively assess the adverse impact of rainstorms on a regional transport network in northern China by measuring the reduction in traffic volume. The model considers four factors: direct and secondary impacts of rainstorms, interdependency between network components, and recovery abilities of cities. We selected the Beijing-Tianjin-Hebei region as the case study area to verify our model.Socioeconomic, precipitation, and traffic volume data in this area were used for model calibration and validation.The case study highlights the potential of the proposed model for rapid disaster loss assessment and risk reduction planning.

Characterizing Uncertainty in City-Wide Disaster Recovery through Geospatial Multi-Lifeline Restoration Modeling of Earthquake Impact in the District of North Vancouver

Restoring lifeline services to an urban neighborhood impacted by a large disaster is critical to the recovery of the city as a whole.Since cities are comprised of many dependent lifeline systems,the pattern of the restoration of each lifeline system can have an impact on one or more others.Due to the often uncertain and complex interactions between dense lifeline systems and their individual operations at the urban scale,it is typically unclear how different patterns of restoration will impact the overall recovery of lifeline system functioning.A difficulty in addressing this problem is the siloed nature of the knowledge and operations of different types of lifelines.Here,a city-wide,multi-lifeline restoration model and simulation are provided to address this issue.The approach uses the Graph Model for Operational Resilience,a data-driven discrete event simulator that can model the spatial and functional cascade of hazard effects and the pattern of restoration over time.A novel case study model of the District of North Vancouver is constructed and simulated for a reference magnitude 7.3 earthquake.The model comprises municipal water and wastewater,power distribution,and transport systems.The model includes 1725 entities from within these sectors,connected through 6456 dependency relationships.Simulation of the model shows that water distribution and wastewater treatment systems recover more quickly and with less uncertainty than electric power and road networks.Understanding this uncertainty will provide the opportunity to improve data collection,modeling,and collaboration with stakeholders in the future.