Nondestructive imaging has been a widely used approach for detection of local structural damage in the engineering
community. By combining image analysis methods, quantities describing the type, severity and extent of damage can
be extracted within the spatial domain of images. However, the current practice of structural health monitoring requires a
temporal characterization of structural damage, or some correlation of structural damage with response data. To accomplish
this, one needs to consider the time scale in using any of the nondestructive imaging techniques, which in turn demands
the use of spatial-temporal image analysis. In this paper, we address the temporal occurrence of cracks on the surface
of concrete structural members, and attempt to monitor cracks, including their inception and propagation, using temporal
image data. We assume under some conditions for objects in a pair of temporal images that only planar rigid-body motion
takes place in the image domain, while cracks are treated as a type of local anomaly. The unknown motion parameters are
estimated by means of a manifold-based optimization procedure, and the obtained manifold distance (MD) measure is used
as a motion-invariant feature to describe the temporal occurrence of concrete cracks. Numerical analyses are conducted
with the use of video clips from two laboratory experiments. It is concluded in this paper that the MD-based spatial-temporal
image analysis can be an effective means for monitoring local damage of structural components that occurs and
is accompanied by structural motion induced by loading.
Tremendous progress has been achieved in image analysis and processing, and in particular, the use of partial differential
equation (PDE) methods in image analysis has proliferated in recent years. However, PDE methods have seen little
application to optical image-based structural damage detection for civil systems. This paper applies the Chan-Vese active
contour model and its level set representation to detect concrete surface damage, in this case concrete cracks, in optical images.
The detected cracks can be further characterized by extracted geometric quantities including their width, length and
area of coverage, and associated with engineering design. With regard to crack width extraction, an approximate method
is proposed, which relies on solving for a signed distance function. We test these methods by using synthetic images as
well as real multi-temporal images from a laboratory experiment. This paper illustrates that using the proposed methods,
cracks with complex topographical patterns can be successfully detected with sufficient accuracy.
The aim of this study is to use observed data from a shaking table test to verify experimentally an SVR-based (support
vector regression) structural identification approach. The method has been developed in previous work and showed
excellent performance for large-scale structural health monitoring in numerical simulations. SVR is a promising data
processing method employing a novel
&egr;-insensitive loss function and the 'Max-Margin' idea. Thus the SVR-based
approach identifies structural parameters accurately and robustly. In this method, a sub-structure technique is used thus
the SVR-based analysis is reduced in dimension. Experimental validation is necessary to verify the method's capability
to identify structural status from real data. For this purpose, a five-floor shear-building shaking table test has been
conducted and two cases, input excitations to the shaking table of the Kobe (NS 1995) earthquake and a Sine wave with
constant frequency and amplitude are investigated.
Multi-temporal earth-observation imagery is now available at sub-meter accuracy and has been found very useful for
performing quick damage detection for urban areas affected by large-scale disasters. The detection of structural damage
using images taken before and after disaster events is usually modeled as a change detection problem. In this paper,
we propose a new perspective for performing change detection, where dissimilarity measures are used to extract urban
structural damage. First, image gradient magnitudes and spatial variances are used as a means to capture urban structural
features. Subsequently, a family of distribution dissimilarity measures, including: Euclidean distance, Cosine, Jeffery
divergence, and Bhattacharyya distance, are used to extract structural damage. We particularly focus on evaluating the
performance of these dissimilarity-based change detection methods under the framework of pattern classification and crossvalidation,
and with the use of a pair of bi-temporal satellite images captured before and after a major earthquake in Bam,
Iran. The paper concludes that the proposed change detection methods for urban structural damage detection, which
are conceptually simple and computationally efficient, outperform the traditional correlation analysis in terms of both
classification accuracy and tolerance to local alignment errors.
Previous experiences during earthquake events emphasize the need for new technologies for real-time monitoring and assessment of facilities with high value nonstructural elements such as equipment or other contents. Moreover, there are substantial limitations to our ability to rapidly evaluate and identify potential hazard zones within a structure, exposing rescue workers, society and the environment to unnecessary risks. A real-time monitoring system, integrated with critical warning systems, would allow for improved channeling of resources. Ideally such a system would acquire all relevant data non-intrusively, at high rates and resolution and disseminate it with low latency over a trusted network to a central repository. This repository can then be used by the building owner and rescue workers to make informed decisions. In recognition of these issues, in this paper, we describe a methodology for image-based tracking of seismically induced motions. The methodology includes calibration, acquisition, processing, and analysis tools geared towards seismic assessment. We present sample waveforms extracted considering pixel-based algorithms applied to images collected from an array of high speed, high-resolution charged-couple-device (CCD) cameras. This work includes use of a unique hardware and software design involving a multi-threaded process, which bypasses conventional hardware frame grabbers and uses a software-based approach to acquire, synchronize and time stamp image data.
The advent of high speed, CCD-based camera technologies opens new possibilities for field monitoring applications. In particular, under natural or man-made loading conditions, applying these new technologies towards the monitoring of building interiors may substantially help rescue and reconnaissance crews during post-event evaluations. To test such a methodology, we have developed a specialized network of high-speed cameras and supporting hardware for monitoring and tracking nonstructural elements subjected to vibration loading, within building structures. Teamed with the University of California, Los Angeles, a full-scale vibration experiment is conducted on a vacant structure damaged during the 1994 Northridge Earthquake. The building of interest is a four-story office building located in Sherman Oaks, California. The investigation has two primary objectives: (1) to characterize the seismic response of an important class of equipment and building contents and (2) to study the applicability of tracking the response of these equipment and contents using arrays of image-based monitoring systems. In this paper, we describe the image acquisition (hardware and software) system and the experimental field set-up are described. In addition, the underlying communication, networking and synchronization of the camera sensor system are discussed.
Remotely sensed satellite imagery of an earthquake-affected area can significantly assist in estimating the severity of infrastructure damage. Modern high-resolution satellite systems have been launched to provide users optical or Synthetic Aperture radar (SAR) data with sub-meter accuracy, which enable the possibility of sensing damage for individual infrastructure by means of pre- and post-event imagery. Herein, we focus our study on the region of Bam, Iran, which was devastated by a moment magnitude Mw = 6.6 earthquake on December 26, 2003, causing approximately 43,200 lives lost. To recognize houses within the Bam region before the earthquake, the boundary of houses are located using a combination of morphological gray-level open and intensity threshold operators. The unique aspect of this paper, as demonstrated with satellite imagery data from this event, is the use of an probabilistic framework for determining the optimal combination of morphological and intensity threshold parameters, which results in an estimated ground truth (EGT). By overlaying the EGT obtained from images before the earthquake with images of the same region after the earthquake, two statistical damage indices, including a new boundary-compactness based index proposed in this study, are compared. This comparison is presented using easily interpretable damage maps, where individual houses are rendered with colors representing the severity of damage.