Quantification of Lost Material Stock of Buildings After An Earthquake. a Case Study of Chiclayo, Peru

Abstract eng:
The occurrence of seismic events that affect urban areas is associated with the generation of large amounts of debris and waste. The presence of debris and waste induces environmental pollution, affects the public health, and impedes the emergency response in the devastated areas. For these reasons, its proper characterization, quantification and subsequent management are of vital importance. This research describes a material stock analysis (MSA) approach used to characterize and quantify the material losses of buildings produced as a consequence of earthquakes. The methodological approach includes the following stages: estimation of building material stock (MS), seismic risk assessment of the studied infrastructure, and estimation of the material losses. In particular, the residential sector of Chiclayo, the fourth most populous city of Peru, was analyzed by combining geographical information systems (GIS) data and statistical data. The analysis concludes that the total MS of buildings in Chiclayo is 24.5 million metric tons (t), equivalent to 49 t per capita. This mass is primarily composed by mineral materials (96.1%), mainly represented by concrete (57.7%), whereas organic materials (wood) and metals (steel) constitute a minor share (3.9%) of the MS. Sanitary and electrical installations, and domestic furniture are excluded from these calculations. Moreover, the expected MS losses of buildings are 2.9, 4.0 and 6.9 million t for the Mw 6.6, Mw 6.9 and Mw 7.4 scenarios, respectively, where adobe represents the largest losses (54.4%, 52.7% and 45.2%). This study demonstrates the significant contribution of MSA in disaster management, providing appropriate information to decision makers during post-disaster response. Also, the resulting MS losses could help to estimate the amount of construction materials needed in the process of reconstruction, as well as the spatial distribution of lost stock could help in prioritize areas of recovery. The results may be especially important on waste management, emergency response and disaster recovery planning.

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