Abstract:The Sumatra Earthquake and Indian Ocean Tsunami event on the 26 December 2004 has provided a unique and valuable opportunity to evaluate the performance of various structures, facilities and lifeline systems during the tsunami wave attacks. There are especially meaningful observations concerning the structural changes due to the tsunami forces, which open up a wide area of research to develop the mitigation procedure. The business restoration process of business companies in terms of buildings, facilities and lifelines have shown greater research interest. In this study, we investigated the restoration process of business sectors in East and South coastal region in Sri Lanka after the 2004 Indian Ocean Tsunami. A field survey was conducted in East and South coast of Sri Lanka, in order to study the affecting parameters to damage assessment in the restoration process of the business companies. The results of the questionnaire-based field survey are then compared with the statistical analysis results. Finally, the factors affecting the restoration process after the tsunami are identified. As a main conclusion, financial support could be OPEN ACCESS Sustainability 2013, 5 457 the most important reason for delays in restoration. Moreover, it has been observed that the tsunami inundation level of higher than one meter may have had more effect concerning the damage to the structures and requires additional time for restoration than other areas.

Purpose -The purpose of this paper is to present a method and results of evaluating damaged building extraction using an object recognition task in pre-and post-tsunami event. The advantage of remote sensing and its applications made it possible to extract damaged building images and vulnerability easement of wide urban areas due to natural disasters. Design/methodology/approach -The proposed approach involves several advanced morphological operators, among which are adaptive transforms with varying size, shape and grey level of the structuring elements. IKONOS-2 satellite images consisting of pre-and post-2004 Indian Ocean Tsunami site of the Kalmunai area on the East coast of Sri Lanka were used. Morphological operation using structural element are applied for segmented images, then extracted remaining building foot print using random forest classification method. This work extended further the road lines extraction using Hough transform. Findings -The result was investigated using geographic information system (GIS) data and global positioning system (GPS) ground survey in the field and it appeared to have high accuracy: the confidence measures produced of a completely destroyed structure give 86 percent by object-based, respectively, after the tsunami in one segment of Maruthamune GN Division. Research limitations/implications -This study has also identified significant limitations, due to the resolution and clearness of satellite images and vegetation canopy over the building footprint. Originality/value -The authors develop an automated method to detect damaged buildings and compare the results with GIS-based ground survey.