Post-Earthquake and Tsunami 3D Laser Scanning

FORENSIC ENGINEERING 2012 © ASCE 2013

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Post-earthquake and tsunami 3D laser scanning forensic investigations Michael J. Olsen1 Robert Kayen2 1

School of Civil and Construction Engineering, Oregon State University, 220 Owen Hall, Corvallis, OR 97331; PH (541) 737-9327; FAX (541) 737-3052; email: [email protected] 2 Pacific Coast & Marine Science Center, USGS, 345 Middlefield Road MS 999, Menlo Park, CA 94025; PH (650) 329-4195; FAX (650) 329-5411; email: [email protected] ABSTRACT The recent Japanese earthquake and tsunami is one of several recent, destructive events that has provided critical engineering information regarding the performance of buildings, bridges, walls, roadways, etc. during intense loading. 3D laser scanning technology provides a valuable tool to acquire perishable information and preserve the scene digitally for post-disaster assessment. This paper discusses the challenges and benefits for use of 3D laser scanning on post-disaster reconnaissance efforts. This paper also focuses on special considerations when performing scanning work in post-disaster environments. Because of the limited time available to make critical decisions, it is important for personnel to know optimal procedures during planning, field reconnaissance, collaboration, data acquisition, processing, and analysis. Examples from recent events illustrate the power of this revolutionary tool in forensic investigations. INTRODUCTION Many recent earthquake and tsunami events have damaged vital public infrastructure and have re-shaped our understanding of natural hazards. Reconnaissance surveys following these events assist engineers and scientists to understand the extents and nature of the damage in order to improve building codes and designs, to identify vulnerable structures and facilities, and to determine the impact of potential hazards. Unfortunately, there is only a limited time window in which to complete these critical observations following an event, particularly in order to avoid interfering with rescue, relief and repair efforts. 3D laser scanning (also known as Light Detection and Ranging, LIDAR) is a new, exciting geospatial technology that can be used to acquire critical geometric information efficiently and with unprecedented detail. Such information is vital to understand civil engineering failures and successes following events of these magnitudes. The powerful, high-resolution, 3D data provides a virtual world that scientists

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and engineers can repeatedly explore, query, and analyze long after restoration and recovery efforts have been completed. A single dataset can also be used for a wide variety of purposes by multiple investigators. Table 1 lists common types of analyses that can be completed with scan data. Figure 1 shows examples of the detail available with 3D laser scan point clouds. These data are valuable information to validate numerical models and theories, enabling the quantification and understanding of earthquake and tsunami forces, failure mode, and structural response with unprecedented detail. These observations and analyses can then be used to calibrate proposed building code design methodologies and practices (Chock et al., 2012). This paper provides considerations for conducting 3D laser scan surveys in post-disaster environments. Limited resources, time, and access can provide challenges to the survey crew in collecting information efficiently and using typical workflows. Further, the large volumes of data produced require significant processing challenges to integrate effectively into traditional structural and geotechnical analyses. Table 1. Sample of types of engineering (Geotechnical and Structural) analyses that can be performed with Laser Scanning data. Geotechnical           

Structural

Liquefaction\Lateral spreading Landslide\slope stability Coastal erosion Settlement Scour (depth distribution and volume) Surface rupture Quay, retaining & sea wall failures Topographic analysis Sediment accretion Subsidence Geomorphic change detection

      

Structural deformations\ displacements\ deflections\ rotations Shear and other crack analysis (orientation, location, distribution, width (larger cracks), etc.) Bridge collapse analysis Spalled concrete quantification Concrete wall blow-out/in failure analysis Permanent soil structure interactions Fatigue analysis

BACKGROUND 3D laser scanning for earthquake reconnaissance 3D laser scanning has recently been applied to several post-disaster earthquake and tsunami damage assessment analyses. Kayen et al. (2010) present an overview of applications and procedures for 3D laser scanning for geotechnical earthquake engineering. The Geo-Engineering Extreme Event Reconnaissance (GEER, http://www.geerassociation.org/) team has been active in deploying 3D laser scanning following major disaster events. A partial list of earthquake reconnaissance reports is shown below:




Figure 1. Examples of laser scan data from Japan and Chile following the earthquakes and tsunamis. (a) Topographic mapping in Onagawa, Japan following the tsunami destruction. (b) Steel frame structure in Yuriage, Japan damaged by tsunami impact. (c) Damaged walls from pressurization at a concrete warehouse in Onagawa, Japan. Tsunami damage to (d) the City Hall and (e) the Capital 100 Hotel in Rikuzentakata, Japan. (f) Deformation analysis of a concrete wall blow-in at the Gamou Wastewater treatment plant. (g) Damage to a concrete deck at a port facility in Talcahuano, Chile.


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•2002 Denali, Alaska. Kayen et al. (2004) combined LIDAR, RADAR and SASW surface wave analyses for a detailed post-earthquake geotechnical investigation.


•2004 and 2007 Nigata. Kayen et al. (2006) performed LIDAR surveys following the earthquake to map surface rupture and liquefaction effects. •2009 American Samoa. Olsen and Donahue (2011) demonstrate the use of LIDAR data to analyze post-earthquake and tsunami damage following the 2009 Samoan earthquake and tsunami, including its ability to generate high resolution topography data for analyzing erosion and scour. •2010 Haiti. Messinger et al. (2010) discuss collection of airborne LIDAR data in Haiti following the earthquake and its role to aid rescue and relief efforts. Cowgill et al. (2010) discuss how these data were used by scientists in an interactive, virtual setting to remotely measure surface rupture and other displacements. •2010 Chile. GEER (2010) performed several geotechnical investigations in conjunction with LIDAR at sites that liquefied and showed site amplification effects. Olsen et al. (2012b) highlights examples of using laser scan data to analyze damaged infrastructure and buildings following the 2010 Chilean earthquake, showing that the laser scan data can detect information that is difficult to observe with traditional reconnaissance techniques. •2011 Tohoku, Japan. Kayen et al. (2011) performed extensive scanning of several sites damaged during the earthquake and tsunami including sites with lateral spreading, slope failures, settlement, embankment failures, seawall failures, port wharf collapse, scouring, and many other types of damage. Olsen et al. (2012a) present LIDAR analyses of structures damaged by the tsunami. Chock et al. (2012) discuss how the LIDAR data can be useful to develop building codes for tsunami impact and inundation forces. Important scanner features What features should one be interested when planning on performing laser scan surveys in disaster environments? Here are some considerations: 1. Fast – Aside from the fact that a faster scan speed will enable the team to cover more ground and acquire more data, the reconnaissance team may be faced with short opportunities to obtain data. These could result from evacuation orders (e.g., a tsunami warning), minimizing the time in an unsafe structure, continual machinery passing for recovery efforts, or an unstable setup location. 2. Long range- The team may not always have access to the site and will need to scan remotely. In many cases, local police forces may block of sections of town to avoid safety hazards, vandalism, and theft. 3. Accuracy – Accuracy needs depend on the potential uses for the data. Generally, most scanners will meet accuracy needs and provide better accuracy (mmlevel relative, cm-level absolute) and resolution (cm-level, although mm-level is


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possible) compared to traditional measurement techniques. Often it is a good idea to acquire basic measurements using standard procedures such as a tape when highest accuracy is needed, particularly on smaller objects (e.g., column width) and would be difficult to obtain from a typical point cloud. 4. Portability, small and lightweight – Smaller equipment will make it easier for the research team to transport and handle all of the equipment. This is also particularly important for storing the equipment during transport and lodging, where accommodations may be tight. Generally, more shorter duration, close range scans will capture topographic information better than a few long range scans due to visibility constraints. However, often one will need long range capabilities when you cannot get access to a site due to safety concerns. Despite recent advances, scanners can be still bulky and expensive to transport. A carnet is a document that can assist the team with bringing the expensive equipment through customs and provide legal documentation in case local authorities question use of the equipment. Comparison of Scanning Platforms Scanning can be accomplished through multiple platforms. Often the platform will be decided based on resource availability given that the time window to mobilize and perform the survey is very short. ALS Airborne laser scanning (ALS) systems have several advantages for reconnaissance. They can acquire data fast and cover large sections of terrain. The data can be geo-referenced on the fly during acquisition, with minor adjustments during processing. However, ALS datasets are of lower accuracy and have a reduced point density compared to STLS and MTLS. Further, the look angle and accuracy are not suitable for detailed building analyses, where it can be difficult to acquire points on the vertical building faces. Additionally, the necessary logistics for data acquisition may prove difficult, particularly immediately following the event. STLS Static Terrestrial Laser Scan (STLS) systems are typically mounted to a tripod and are a slower method of collecting data for a large area. However, STLS offers improved resolution and accuracy for analyses of small sites or buildings compared to airborne systems. It can also fill in gaps from mobile or airborne LIDAR datasets. Finally, the STLS datasets require more user-interaction for geo-referencing and processing. Typically, reflective targets are used for scan alignment and GPS coordinates for these targets or setup locations can enable the scans to be geo-referenced. MTLS Mobile Terrestrial Laser Scan (MTLS) systems can acquire data fast from a moving vehicle. Geo-referencing can be completed directly with the combination of components for the scanner. However, scan extents are limited to the roadway +/-


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100 m, and scanning may not be feasible if the roadway is significantly damaged and non-navigable. Further, the systems are much more difficult to transport equipment than STLS and may require calibration on site prior to data acquisition if disassembled. Note that other “mobile” systems such as helicopter, ATV, or boat may be more suitable and faster for acquisition, depending on the application. METHODOLOGY Collins et al. (2007), Kayen et al. (2010) and Olsen et al. (2008; 2010; 2011) provided an overview of typical laser scan acquisition and processing workflows, including discussion of common topographic data products. As such, this paper only provides a brief overview and focuses on details specific to post disaster reconnaissance. The key stages of a post-disaster workflow are presented in Figure 2.

Figure 2. Flow chart depicting scan workflow for reconnaissance work (Modified from Olsen et al., 2010) Planning and Collaboration As most disasters are unpredictable, the reconnaissance team has limited time to prepare for the effort and gather supplies. Orchestrating an efficient scan plan immediately following an earthquake can be very difficult. The research team is not often familiar with the geography of the area and has limited information related to locations of most damage. Hence, local researchers are a valuable asset to any reconnaissance mission. Aside from familiarity with the area, they have immense knowledge regarding the conditions before the event and also the extent of damage. Timing of the survey is critical as arrival too early will result in difficulty obtaining access and could hamper recovery efforts. Arrival too late means that the scan team will be unable to acquire perishable data as repairs and recovery efforts are underway.


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Additionally, the reconnaissance crew can provide important information to recovery efforts such as damage maps since emergency personnel are often overwhelmed during this time. Preliminary Reconnaissance Satellite imagery and media can help identify areas of top priority. Virtual exploration through Google Earth or other public information sources can assist in some cases. A preliminary team can perform a quicker, broad-sweeping survey of the affected region and then feed that information back to the laser scan team to perform detailed surveys of a reduced number of sites of highest priority. Data acquisition As described previously, laser scanners can be mounted to a variety of platforms. Mobile (airborne, vehicle, helicopter, boat, etc.) scan systems can acquire overall topography faster, so terrestrial crew can focus on scanning areas requiring more detail. Where significant damages have occurred to roadways, vehicle mounted scanners may not be an option until repairs are completed. In an ideal world, data from mobile platforms would be employed as soon as possible so that information can be transmitted to responding agencies quickly regarding the state and extent of damage. Further, future ground based crews can virtual navigate the site and determine priority locations before travelling. However, current data dissemination infrastructure limitations lead to significant delays between data acquisition and availability to the public. NOAA Digital Coast, USGS Click, and OpenTopography are examples of web-infrastructure that have made some of these data publicly available. Scanning in a post-disaster environment can be significantly challenging. Generally, the team is unfamiliar with the area and where to purchase supplies. The terrain is covered with debris which reduces visibility (requiring more scan positions), presents safety challenges, and can require substantial processing to eliminate from the dataset. Often, power will not be available in the areas of interest. A scan team can work 2-3 days using the power available in a typical car battery. It is also not possible to bring all of the traditional tools that one would have available on a typical project. After a disaster, it can be difficult for local economies to recover. The reconnaissance team can help through purchases. However, the team must always be sensitive to local needs so that they are not competing with the local population for supplies and should purchase supplies out of the area of interest. Geo-referencing data can be challenging for point clouds that have to be collected in limited field time. The team may have little knowledge about existing control networks. Further, most existing control networks are disrupted from ground displacements. While targets are typically used for the alignment of static scans, targets can add significant time to field acquisition and require the transport and setup of additional equipment. Hence, the team must decide whether it is more important to have the quality assurance and ease of alignment provided by targets, or if it is more important to acquire as much data as possible and align the data through software cloud to cloud techniques, which enable quicker field acquisition but may reduce the alignment accuracy. The amount of noise in the data can be problematic for cloud to cloud registration techniques.


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Post-processing Data cleaning and filtering can be very time consuming. It can be difficult to separate debris and important features in the point cloud (Figure 2). Many automated algorithms for feature recognition and processing are still in their infancy and do not work well on damaged objects. Debris also will often block the line of sight during acquisition, leaving significant occlusions. Dust, debris and moisture can generate spurious points throughout the scene. Often these can be removed using an automated, isolated points filter, which will remove points from the dataset that do not have a neighbor within a user-specified threshold (e.g., 1m).

Figure 3. Point cloud of a steel structure in Japan (a) before and (b) after manual cleaning. Analyses Currently, there is a large disconnect between the laser scan data and typical engineering analysis platforms. Many engineering analysis packages are not designed to handle the large volumes of data provided through scanning. Further, the irregularity of the data spacing and occlusions provides a significant challenge to perform analyses, which typically require simple, complete, idealized models. However, measurements from LIDAR data can be used to construct and validate these numerical models. Further, new tools are constantly developing that enable more advanced analyses with scan data. For example, as shown in Figure 1 (e&f), detailed change and deformation analyses can be performed providing insights on failure mechanisms and the distribution of damage to a structure or site. Unfortunately, during analysis there are minimal data available for the “before” conditions. Because 3D laser scanning is a new technology, datasets are only available for short term studies, whereas many geologic processes occur over very large time scales. Baseline information generally is not available at the same resolution as the acquired data following a disaster; hence, many studies must infer conditions prior to damage analysis. To remedy this, baseline data should be continually collected at higher resolutions to identify at risk areas such that mitigation efforts can be prioritized. In addition to quantitative analysis, distance learning is also an important aspect of analyzing 3D laser scanning data. Other researchers can use these data to visualize impacts and deformations and place measurements and observations in context with one another. For example, the USGS has developed several animations [http://vimeo.com/lidarmedialab] that are, in and of themselves, not useful for the actual engineering analysis and calculations; however, they provide an excellent means


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for orienting people to the problems. Virtual site visits can be experienced through fly-through, three-dimensional animations enabling both the scientific community and the general public to obtain a better appreciation for the scale of damage through these web-based presentations of the data.


CONCLUSIONS 3D laser scanning has a lot to offer for forensic investigations following disasters including detailed deformation measurements, virtual scene recreation, and a digital record that can be used for a variety of applications. However, as most new technology, 3D laser scanning has several challenges including equipment, large data sizes, and substantial processing time. Scanning in post disaster environments is consistently a trade-off between the quality and quantity and quantity of data collected. Hence, the survey crew needs to balance these to obtain data for as many sites that will be of sufficient quality for the anticipated analyses. Ultimately, one should select the best tool for the job. Scanning has a lot to offer, but at some sites other tools may acquire sufficient data quicker than scanning will. For many types of analyses, scanning provides more information than can be used in current models. For instance, many engineering analyses are currently not 3D analyses and often work with cross sections. Data from LIDAR will help enable the profession to develop more advanced, 3D techniques for engineering analysis. ACKNOWLEDGEMENTS Funding for the Japanese LIDAR data was provided by the National Science Foundation (NSF) through Rapid Grants 1138710 and 1138699. Leica Geosystems and Maptek I-Site provided software used for the data analyses. We thank GEER for their efforts in providing post-disaster information to the public and organizing several LIDAR survey campaigns. We thank the Building Research Institute (BRI) for their help with the ground surveys. Asia Air Survey, Co. provided mobile laser scan system data for Onagawa. Oregon State University students Evon Silvia and Shawn Butcher performed the LiDAR data collection in Japan. REFERENCES Chock, G., Robertson, I., Carden, L., and Yu, G. (2012). “Tohoku tsunami-induced building damage analysis including the contribution of earthquake resistant design to tsunami resilience of multi-story buildings,” Proceedings of the international symposium on Engineering Lessons Learned from the 2011 Great East Japan Earthquake, (March 1-4, 2012), Tokyo, Japan, 492-503. Collins, B., Kayen, R., Reiss, T., and Sitar, N. (2007). “Terrestrial LiDAR investigation of the December 2003 and January 2007 activations of the Northridge Bluff Landslide, Daly City, California.” USGS, Open File Rep. 2007-1079, 32pg.


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Cowgill, E., Bernardin, T.S., Oskin, M.E., Bowles, C., Yikilmaz, M.B., Kreylos, O., Elliot, A.J., Bishop, M.S., and Kellog, L.H., 2010, Analysis of LIDAR data during rapid scientific response to the January 12, 2010 Haiti earthquake, 2010 Geological Society of America Abstracts with Programs, 42(5), 154. GEER (2010). “Geo-Engineering Reconnaissance of the February 27, 2010 Maule, Chile Earthquake,” GEER Association Report No. GEER-022. Kayen, R.,, Barnhardt, W., Carkin, B., Collins, B.D., Grossman, E.E., Minasian, D.,Thompson, E. (2004) Imaging the M7.9 Denali Fault Earthquake 2002 Rupture at the Delta River Using LIDAR, RADAR, and SASW Surface Wave Geophysics, Eos Trans. AGU,85(47), Fall Meet. Suppl., Abstract S11A-0999. Kayen, R., Pack, R.T., Bay, J., Sugimoto, S., Tanaka, H., (2006). “Terrestrial-LiDAR visualization of surface and structural deformations of the 2004 Nigata Ken Chuetsu Japan, Earthquake,” Earthquake Spectra, EERI, 22(S1), S147-S162. Kayen, R., Stewart, J.P., Collins, B., (2010). “Recent advances in terrestrial LiDAR applications in geotechnical earthquake engineering,” Proc. 5th Int. Conf. on Recent Advances in Geotech. Earthquake Engrg and Soil Dynamics, ASCE. Kayen, R., Tanaka, Y, Tanaka, H, Sugano, T., Estevez, I.A., Cullenward, S.S., Yeh, W., Thomas, D. (2011). “LiDAR and Field Investigation of the March 11, 2011 M9.0 Great Tohoku Offshore Earthquake, and April 7, 2011 M7.4 Aftershock,” GEER Association Report No. GEER025f. Messinger, D.W., van Aardt, J., McKeown, D., Casterline, M., Faulring, J., Raqueño, J., Basener, B., and Velez-Reyes, M., 2010, High-resolution and LIDAR imaging support to the Haiti earthquake relief effort, Proc. SPIE 7812, 78120L; doi:10.1117/12.862090 Olsen, M.J., Johnstone, E., Young, A.P., Hsieh, T.J., Ashford, S.A., Driscoll, N., & Kuester, F. (2008). “Rapid Response to Seacliff Erosion in San Diego County using Terrestrial LIDAR,” Proc. Solutions to Coastal Disasters Conference, ASCE, Oahu, Hawaii (April 13-16, 2008), p. 573-583. Olsen, M.J., Kuester, F., Chang, B., & Hutchinson, T. (2010). “Terrestrial laser scanning based structural damage assessment,” Journal of Computing in Civil Engineering, 24(3), 264-272. Olsen, M.J., and Donahue, J., (2011). “A wave of new information: LiDAR investigations of the 2009 Samoan tsunami,” Proc. Sol. to Coastal Disasters Conf., ASCE, 321-330. Olsen, M.J., Carden, L., Silvia, E.P., Chock, G., Robertson, I.N., & Yim, S. (2012a). “Capturing the impacts: 3D laser scanning following the Tohoku earthquake and tsunami,” Proceedings of the 9th CUEE and 4th ACEE Joint Conference, Tokyo Institute of Technology, Japan. Olsen, M.J., Cheung, K.F., Yamazaki, Y., Butcher, S.M., Garlock, M., Yim, S.C., Piaskowy, S., Robertson, I., Burgos L., and Young Y.L. (2012b, In Press). “Damage Assessment of the 2010 Chile Earthquake and Tsunami using ground-based LIDAR,” Earthquake Spectra.


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