A Novel Application of Photogrammetry for Retaining Wall Assessment

Retaining walls are critical geotechnical assets and their performance needs to be monitored in accordance to transportation asset management principles. Current practices for retaining wall monitoring consist mostly of qualitative approaches that provide limited engineering information or the methods include traditional geodetic surveying, which may provide high accuracy and reliability, but is costly and time-consuming. This study focuses on evaluating failure modes of a 2.43 m × 2.43 m retaining wall model using three-dimensional (3D) photogrammetry as a cost-effective quantitative alternative for retaining wall monitoring. As a remote sensing technique, photogrammetry integrates images collected from a camera and creates a 3D model from the measured data points commonly referred to as a point cloud. The results from this photogrammetric approach were compared to ground control points surveyed with a total station. The analysis indicates that the accuracy of the displacement measurements between the traditional total station survey and photogrammetry were within 1–3 cm. The results are encouraging for the adoption of photogrammetry as a cost-effective monitoring tool for the observation of spatial changes and failure modes for retaining wall condition assessment.

[1]  Youssef M A Hashash,et al.  Integration of Construction As-Built Data Via Laser Scanning with Geotechnical Monitoring of Urban Excavation , 2006 .

[2]  David V. Jáuregui,et al.  Noncontact Photogrammetric Measurement of Vertical Bridge Deflection , 2003 .

[3]  Ivan Bartoli,et al.  Use of Unmanned Aerial Vehicle for Quantitative Infrastructure Evaluation , 2015 .

[4]  Marco Scaioni,et al.  Monitoring of a SFRC retaining structure during placement , 2010 .

[5]  Tareq Rasheed A Mohammad Failure of a Ten-Storey Reinforced Concrete Building Tied To Retaining Wall: Evaluation, Causes, and Lessons Learned , 2005 .

[6]  Thomas Oommen,et al.  Interferometric Stacking toward Geohazard Identification and Geotechnical Asset Monitoring , 2016 .

[7]  Daniel Alzamora,et al.  Asset Management Systems for Retaining Walls , 2008 .

[8]  Thomas Oommen,et al.  Unmanned Aerial Vehicle (UAV)-Based Assessment of Concrete Bridge Deck Delamination Using Thermal and Visible Camera Sensors: A Preliminary Analysis , 2018 .

[9]  Scott A. Anderson,et al.  Capturing The Impacts of Geotechnical Features on Transportation System Performance , 2013 .

[10]  Luc Chouinard,et al.  Ranking Models Used for Condition Assessment of Civil Infrastructure Systems , 1996 .

[11]  Jianwei Gong,et al.  Mobile Terrestrial Laser Scanning for Highway Inventory Data Collection , 2012 .

[12]  Fei Dai,et al.  Comparison of Image-Based and Time-of-Flight-Based Technologies for Three-Dimensional Reconstruction of Infrastructure , 2013 .

[13]  Ali Khaloo,et al.  Hierarchical Dense Structure-from-Motion Reconstructions for Infrastructure Condition Assessment , 2017, J. Comput. Civ. Eng..

[14]  Mohammed A. Gabr,et al.  Retaining Wall Field Condition Inspection, Rating Analysis, and Condition Assessment , 2016 .

[15]  Luigi Barazzetti,et al.  Image-based deformation measurement , 2015 .

[16]  Olivier D. Faugeras,et al.  The geometry of multiple images - the laws that govern the formation of multiple images of a scene and some of their applications , 2001 .

[17]  Xiufeng He,et al.  GPS and InSAR Time Series Analysis: Deformation Monitoring Application in a Hydraulic Engineering Resettlement Zone, Southwest China , 2013 .

[18]  Steve Wendland When Retaining Walls Fail , 2011 .

[19]  Jan Boehm,et al.  Close-Range Photogrammetry and 3D Imaging , 2013 .

[20]  Bernhard P. Wrobel,et al.  Multiple View Geometry in Computer Vision , 2001 .

[21]  P. Wolf,et al.  Elements of Photogrammetry(with Applications in GIS) , 2000 .

[22]  Ioannis Brilakis,et al.  Comparison of Optical Sensor-Based Spatial Data Collection Techniques for Civil Infrastructure Modeling , 2009 .

[23]  Luigi Barazzetti,et al.  Photogrammetric techniques for monitoring tunnel deformation , 2014, Earth Science Informatics.

[24]  Mani Golparvar-Fard,et al.  Evaluation of image-based modeling and laser scanning accuracy for emerging automated performance monitoring techniques , 2011 .

[25]  Debra F. Laefer,et al.  Viability assessment of terrestrial LiDAR for retaining wall monitoring , 2008 .

[26]  Mani Golparvar-Fard,et al.  Segmentation and Recognition of Highway Assets Using Image-Based 3D Point Clouds and Semantic Texton Forests , 2015, J. Comput. Civ. Eng..

[27]  Michael J. Olsen,et al.  Real-time change and damage detection of landslides and other earth movements threatening public infrastructure. , 2012 .

[28]  J. Erik Loehr,et al.  Asset Management Framework for Geotechnical Infrastructure , 2003 .

[29]  Bing Yang,et al.  Applications of structure from motion: a survey , 2013, Journal of Zhejiang University SCIENCE C.

[30]  Burcin Becerik-Gerber,et al.  Automated Cleaning of Point Clouds for Highway Retaining Wall Condition Assessment , 2014 .

[31]  Joseph Wartman,et al.  Principles and Applications of Digital Photogrammetry for Geotechnical Engineering , 2006 .

[32]  George Vosselman,et al.  Airborne and terrestrial laser scanning , 2011, Int. J. Digit. Earth.

[33]  Roderik Lindenbergh,et al.  Change detection and deformation analysis using static and mobile laser scanning , 2015 .

[34]  Fabio Remondino,et al.  Image‐based 3D Modelling: A Review , 2006 .

[35]  F. Rivera,et al.  The Integration of TLS and Continuous GPS to Study Landslide Deformation: A Case Study in Puerto Rico , 2010 .

[36]  Christian Whler 3D Computer Vision: Efficient Methods and Applications , 2009 .

[37]  Paul D Thompson,et al.  Assessment of Retaining Wall Inventories for Geotechnical Asset Management , 2015 .

[38]  Anthony T. C. Goh,et al.  Reliability assessment of serviceability performance of braced retaining walls using a neural network approach , 2005 .

[39]  Richard J. Dobson,et al.  Evaluation of Commercially Available Remote Sensors for Highway Bridge Condition Assessment , 2012 .

[40]  Jung-Geun Han,et al.  Application of a Photogrammetric System for Monitoring Civil Engineering Structures , 2012 .

[41]  Gianfranco Forlani,et al.  Terrestrial photogrammetry without ground control points , 2014, Earth Science Informatics.

[42]  Nicola Casagli,et al.  Landslide mapping and monitoring by using radar and optical remote sensing: examples from the EC-FP7 project SAFER , 2016 .

[43]  Burcin Becerik-Gerber,et al.  Automated measurement of highway retaining wall displacements using terrestrial laser scanners , 2016 .

[44]  Ralf Dresner Soils And Foundations For Architects And Engineers , 2016 .

[45]  Kenneth R. White,et al.  Close-range photogrammetry applications in bridge measurement: Literature review , 2008 .

[46]  Amit Armstrong,et al.  Retaining Walls Are Assets Too , 2009 .

[47]  Chester I. Duncan Soils and Foundations for Architects and Engineers , 1993 .

[48]  M. Westoby,et al.  ‘Structure-from-Motion’ photogrammetry: A low-cost, effective tool for geoscience applications , 2012 .