David Lattanzi

Measures of the Seismic Vulnerability of Reinforced Concrete Buildings in Haiti

Following the 12 January 2010 Haiti earthquake, teams of students and faculty members from the United States and Haiti surveyed 170 reinforced concrete (RC) buildings in Port-au-Prince and Léogâne. This paper summarizes the survey results and compares them with results from a similar survey done after the 1999 earthquakes near Düzce, Turkey. The survey results demonstrate that the frequency of damage in RC buildings was higher in Haiti than in Turkey. This increased level of damage is consistent with practical screening criteria based on cross-sectional areas of building columns and walls. Based on these criteria, 90% of the structures surveyed in Haiti would have been classified as seismically vulnerable before the earthquake. Damage was more frequent in structures with captive columns. A two-tiered screening process is suggested to rapidly assess the vulnerability of scores of poorly built low-rise RC buildings in future earthquakes.

3D Scene Reconstruction for Robotic Bridge Inspection

AbstractThis paper provides a comparative study of two methods for reconstructing three-dimensional (3D) scenes from monocular two-dimensional (2D) images with respect to their applicability to rob...

Hierarchical Dense Structure-from-Motion Reconstructions for Infrastructure Condition Assessment

AbstractAccurate condition assessment of in-service infrastructure systems is critical for system-wide prioritization decisions. Current protocols require lengthy inspections and expensive equipment to examine large infrastructure systems. Furthermore, changes in inspection protocols over time can create discontinuities in recording and understanding the time history of a structure. To address these challenges, a systematic and adaptive technique for converting two-dimensional (2D) digital images into three-dimensional (3D) models has been developed, with the goal of creating high-resolution and scale-accurate inspection records. The developed reconstruction technique utilizes multiscale imaging to reconstruct a structure with varying levels of details and geometric complexity. The captured images are then converted into photorealistic, accurate, and dense 3D scene reconstructions by utilizing a hierarchical adaptation of a dense structure-from-motion (DSfM) algorithm. The result of this approach is a vir...

Robust Automated Concrete Damage Detection Algorithms for Field Applications

This paper presents a computer vision framework supporting automated infrastructure damage detection, with a specific focus on surface crack detection in concrete. The approach presented is designed to provide a significant increase in robustness relative to existing methods when faced with widely varying field conditions while operating fast enough to be used in large scale applications. In particular, a clustering method for segmentation is developed that exploits inherent characteristics of fracture images to achieve consistent performance, combined with robust feature extraction to improve recognition algorithm classifier outcomes. The approach is shown to perform well in detecting cracks across a broad range of surface and lighting conditions, which can cause existing techniques to exhibit significant reductions in detection accuracy and/or detection speed.

Review of Robotic Infrastructure Inspection Systems

AbstractIn order to minimize the costs, risks, and disruptions associated with structural inspections, robotic systems have increasingly been studied as an enhancement to current inspection practic...

Unmanned aerial vehicle inspection of the Placer River Trail Bridge through image-based 3D modelling

Abstract Unmanned aerial vehicles (UAV) are now a viable option for augmenting bridge inspections. Utilising an integrated combination of a UAV and computer vision can decrease costs, expedite inspections and facilitate bridge access. Any such inspection must consider the design of the UAV, the choice of cameras, data acquisition, geometrical resolution, safety regulations and pilot protocols. The Placer River Trail Bridge in Alaska recently served as a test bed for a UAV inspection methodology that integrates these considerations. The end goal was to produce a three-dimensional (3D) model of the bridge using UAV-captured images and a hierarchical Dense Structure-from-Motion algorithm. To maximise the quality of the model and its benefits to inspectors, this goal guided UAV design and mission planning. The resulting inspection methodology integrates UAV design, data capture and data analysis together to provide an optimised 3D model. This model provides inspection documentation while enabling the monitoring of defects. The developed methodology is presented herein, as well as analyses of the 3D models. The results are compared against models generated through laser scanning. The findings demonstrate that the UAV inspection methodology provided superior 3D models with the accuracy to resolve defects and support the needs of infrastructure managers.

A Prototype Imaging and Visualization System for Robotic Infrastructure Inspection

Civil inspection images are notoriously difficult to interpret for those who were not present during an inspection, and so they have historically had limited value in ongoing condition monitoring applications. Within the last decade, the advent of inexpensive digital imaging has compounded this problem by making it possible and common to capture and report numerous images, creating information overload that can easily overwhelm engineers and structure owners. More recently, developments in electronics manufacturing and robotics have made automated structural inspection an emergent technology for civil engineers, which inevitably is leading to even more digital image data that must be handled. This paper presents a systematic view of capturing, processing, and representing inspection image data such that the inherent value of these increasingly large data sets can be realized. Using a data pipeline combining automated image capture, contextualized 3D visualization, and robust computational imaging and data fusion techniques, the goal is to allow engineers to view inspection images in their 3D spatial context, while aiding them through enhanced damage detection routines, all while helping to minimize field inspection disruptions through the use of robotic imaging. In the development of this pipeline and an associated prototype implementation, several key issues have been addressed: (i) automated systems capable of comprehensive field imaging; (ii) 3D reconstruction algorithms which provide accurate , photorealistic image interpretations; (iii) robust computer vision algorithms suitable for field applications; and (iv) data fusion models which correlate the relationships between extracted image information and structural performance. The results of prototype testing show that, given due consideration to the inherently large data sets that robots produce, automated inspection can enable entirely new ways of visualizing and interacting with inspection information.

Robust normal estimation and region growing segmentation of infrastructure 3D point cloud models

Modern remote sensing technologies such as three-dimensional (3D) laser scanners and image-based 3D scene reconstruction are in increasing demand for applications in civil infrastructure design, maintenance, operation, and as-built construction verification. The complex nature of the 3D point clouds these technologies generate, as well as the often massive scale of the 3D data, make it inefficient and time consuming to manually analyze and manipulate point clouds, and highlights the need for automated analysis techniques. This paper presents one such technique, a new region growing algorithm for the automated segmentation of both planar and non-planar surfaces in point clouds. A core component of the algorithm is a new point normal estimation method, an essential task for many point cloud processing algorithms. The newly developed estimation method utilizes robust multivariate statistical outlier analysis for reliable normal estimation in complex 3D models, considering that these models often contain regions of varying surface roughness, a mixture of high curvature and low curvature regions, and sharp features. An adaptation of Mahalanobis distance, in which the mean vector and covariance matrix are derived from a high-breakdown multivariate location and scale estimator called Deterministic MM-estimator (DetMM) is used to find and discard outlier points prior to estimating the best local tangent plane around any point in a cloud. This approach is capable of more accurately estimating point normals located in highly curved regions or near sharp features. Thereafter, the estimated point normals serve a region growing segmentation algorithm that only requires a single input parameter, an improvement over existing methods which typically require two control parameters. The reliability and robustness of the normal estimation subroutine was compared against well-known normal estimation methods including the Minimum Volume Ellipsoid (MVE) and Minimum Covariance Determinant (MCD) estimators, along with Maximum Likelihood Sample Consensus (MLESAC). The overall region growing segmentation algorithm was then experimentally validated on several challenging 3D point clouds of real-world infrastructure systems. The results indicate that the developed approach performs more accurately and robustly in comparison with conventional region growing methods, particularly in the presence of sharp features, outliers and noise.

Long-term monitoring of structures through point cloud analysis

Modern remote sensing technologies have enabled the creation of high-resolution 3D point clouds of infrastructure systems. In particular, photogrammetric reconstructions using Dense-Structure-from-Motion algorithm can now yield point clouds with the necessary resolution to capture small-strain displacements. By tracking changes in these point clouds over time, displacements can be measured, leading to strain and stress estimates for long-term structural evaluations. This study determines the accuracy of a comparative point cloud analysis technique for measuring deflections in high-resolution point clouds of structural elements. Utilizing a combination of a recently developed point cloud generation process and localized nearest-neighbors cloud comparisons, the analytical technique is designed for long-term field scenarios and requires no artificial tracking, targets, and camera calibrations. A series of flexural laboratory experiments were performed in order to test the approach. The results indicate sub-millimeter accuracy in measuring the vertical deflection, making it suitable for the small-displacement analysis of a variety of large-scale infrastructure systems. Ongoing work seeks to extend this technique for comparison with as-built and finite element models.

Extracting Structural Models through Computer Vision

The ability to accurately and rapidly assess structural integrity after a disaster is critical from both a safety and economic perspective. Existing post-disaster inspection methods are time-consuming and expensive, requiring highly trained inspectors to travel to target sites and manually collect data. Automated analysis of civil structures from visual data through computer vision can be used to improve the level of accuracy in the condition assessment procedure. This paper presents a method of automated and systematic computer vision-based structural analysis. It uses a set of digital photographs to produce a 3D model through Structure from Motion (SfM) algorithms, followed by fully automated recognition and assembly of structural elements (e.g., columns and beams) from the image-based 3D dense reconstruction of the structure. There are three key challenges in this work: (i) proper 3D mesh segmentation, (ii) robust computer vision algorithms for isolating different structural components, and (iii) classification and localization of damage that is present in the 3D model. As the part of the proposed system, extracted information from the dense 3D model is used to assemble the structural elements and create a Finite-Element Method (FEM) model. Lastly, a supervised machine learning scheme built upon a large and comprehensive data set is used to automatically update the model to account for damage. The proposed methodology has applications beyond post-disaster condition assessment, from routine inspection to infrastructure management applications.