Alireza G. Kashani

A Review of LIDAR Radiometric Processing: From Ad Hoc Intensity Correction to Rigorous Radiometric Calibration

In addition to precise 3D coordinates, most light detection and ranging (LIDAR) systems also record “intensity”, loosely defined as the strength of the backscattered echo for each measured point. To date, LIDAR intensity data have proven beneficial in a wide range of applications because they are related to surface parameters, such as reflectance. While numerous procedures have been introduced in the scientific literature, and even commercial software, to enhance the utility of intensity data through a variety of “normalization”, “correction”, or “calibration” techniques, the current situation is complicated by a lack of standardization, as well as confusing, inconsistent use of terminology. In this paper, we first provide an overview of basic principles of LIDAR intensity measurements and applications utilizing intensity information from terrestrial, airborne topographic, and airborne bathymetric LIDAR. Next, we review effective parameters on intensity measurements, basic theory, and current intensity processing methods. We define terminology adopted from the most commonly-used conventions based on a review of current literature. Finally, we identify topics in need of further research. Ultimately, the presented information helps lay the foundation for future standards and specifications for LIDAR radiometric calibration.

Failure Progression Analysis of Observed Residential Structural Damage within a Tornado Wind Field

Data collected from recent tornadoes in Tuscaloosa, Joplin, and Moore shows a consistent pattern of damage to residential structures. For an EF-4 or EF-5 tornado, damage levels increase from the outer edges toward to the center line of a tornado track. This is not just because of higher wind speeds at the center of a tornado vortex; the wind velocity fields around structures are also different at the tornado center. Analysis from the damage pattern from the tornado showed that the failure progression of residential structures within a tornado wind field depends on the relative location and direction of the house to the tornado track. With the same wind speed, different damage levels can be observed if structures located in different relative distances from the center-line of a tornado damage track. This should be considered when predicting tornado wind speed based on residential structural damage.

Lidar-Based Methodology to Evaluate Fragility Models for Tornado-Induced Roof Damage

AbstractStructural fragility models have been developed to provide probabilistic estimates of building component damage states caused by exposure to differing wind speeds. Limitations in taking accurate measurements and gathering quantitative loss information in the aftermath of tornadoes has not allowed for comparing fragility-based information with actual building-loss data. This paper presents a methodology for extracting damaged building geometry and roof sheathing loss information from three-dimensional (3D) point cloud data collected with laser scanning technology. The observed building component damage states are then compared to fragility model predictions to validate the models. The methodology was tested on 3D data collected after the 2013 Moore, Oklahoma tornado. From this initial study, which was performed on 5 houses having 27 damaged roof surfaces, it was shown that fragility models underestimated the loss in the center of roof planes (Zone 1) and overestimated the loss in the corners of roo...

3D Data Collection and Automated Damage Assessment for Near Real-time Tornado Loss Estimation

Tornadoes cause great hardship and economic loss to US communities each year. The lack of quantitative information associated with real damage states of individual buildings results in inaccurate loss estimates and hampers effective decision making for mitigation, response and recovery. A near real-time tornado loss estimation tool is developed and tested as part of this work. The GIS-based damage assessment tool employs post-event point cloud data collected by terrestrial scanners and pre-event aerial images. The tool automatically calculates the percentage of roof and wall damage at the individual building scale, which is used as input to empirical or statistical loss estimation methods. An accuracy analysis through a set of controlled experiments indicated that for typical point cloud density (>25 points/m 2 ), the tool results in less than 10% error in detection of pre- and post-event roof/wall surfaces. The GIS-based tool was validated with datasets collected after the 2013 Moore, OK tornado and produced detailed percentage of damage for buildings, which was not provided by infield inceptions.

Assessing, Coding, and Marking of Highway Structures in Emergency Situations, Volume 2: Assessment Process Manual

A recent survey of state highway agencies (SHA) indicated that only 22% of SHAs have a formal procedure for assessing highway structures in emergency situations. 30% of SHAs indicated that they have an informal process whereas 40% of SHAs do not have any assessment process at all. Furthermore, very large scale disaster events, such as hurricanes or earthquakes, are likely to cross state borders and rapidly deplete local resources. These events often require resources from other states to assist in the recovery efforts and coordination with multiple entities. To minimize confusion during response and maximize community resilience, it is important for all states to utilize the same approach to assessing, coding, and marking of highway structures in emergency situations. This paper describes the detailed process for assessing, coding, and marking of highway structures in emergency situations that was developed in NCHRP Report 833. This process is based on best practices from across the United States and is applicable to a wide range of highway structures and emergency situations. Associate Professor, Department of Civil Engineering, Merrimack College, North Andover, MA 01810 (email: veletzosm@merrimack.edu) Associate Professor, School of Civil and Construction Engineering, Oregon State University, Corvallis, OR 97331 (email: olsen@oregonstate.edu) Assistant Professor, School of Civil and Construction Engineering, Oregon State University, Corvallis, OR 97331 (email: andre.barbosa@oregonstate.edu) Design Engineer, Magnusson Klemencic Associates, Seattle, WA. 98101 (email: patrickburns32@gmail.com) Associate Professor, Dept. of Civil and Mechanical Engineering, University of Missouri-Kansas City, Kansas City, MO 64110 (email: chenzhiq@umkc.edu) Principal Engineer, MPN Components, Hampton, NH 03842 (email: gene.roe@lidarnews.com) Executive Vice-President, Advanced Infrastructure Design, Inc., Hamilton Township, NJ 08691 (email: ktabrizi@aidpe.com) Veletzos M, Olsen, MJ, Barbosa AR, Burns P, Chen Z, Roe G, Tabrizi K. Assessing, Coding and Marking of Highway Structures in Emergency Situations. Proceedings of the 11 National Conference in Earthquake Engineering, Earthquake Engineering Research Institute, Los Angeles, CA. 2018. ASSESSING, CODING AND MARKING OF HIGHWAY STRUCTURES IN EMERGENCY SITUATIONS M. Veletzos, M.J. Olsen, A.R Barbosa, P. Burns, Z. Chen, G. Roe, and K. Tabrizi