Modelling of waterfront cantilever sheet pile walls subjected to earthquake loading

The seismic performance of waterfront cantilever sheet pile retaining walls is of continuing interest to geotechnical engineers as these structures suffer severe damage and even complete failure during earthquakes. This is often precipitated by liquefaction of the surrounding soil, either in the backfill or in front of the wall. This paper presents results from a series of small-scale plane strain models that were tested on a 1-g shaking table and recorded using a high-speed, high-resolution digital camera. The technique of Particle Image Velocimetry (PIV) was applied in order to allow the failure mechanisms to be visualised. It is shown that using PIV analyses it is possible to obtain failure mechanisms for a cantilever wall in liquefiable soil. These failure mechanisms are compared with those obtained for a cantilever wall in dry soil, previously carried out at a similar scale. It was observed that seismic liquefaction causes significant displacement in much larger zones of soil near the retaining wall compared to an equivalent dry case. The failure mechanism for a cantilever wall with liquefiable backfill, but with a remediated zone designed not to liquefy, is also presented and compared to the unremediated case.

Development of a servo-hydraulic earthquake actuator for the Cambridge Turner beam centrifuge

Dynamic centrifuge modelling has been carried out at Cambridge since the late 1970s. Over this period, three different mechanical earthquake actuators were developed. In this paper the development of a new servo-hydraulic earthquake actuator is described. The basic design principles are explained along with the need to carry out these designs to match the existing services and systems of the 35 year old Turner beam centrifuge at Cambridge. In addition, some of the features of the Turner beam centrifuge are exploited in the design of this new earthquake actuator. The paper also explains the mechanical fabrication of the actuator and the control systems that were developed in order to generate real earthquake motions. Finally, the performance of this new servo-hydraulic earthquake actuator is presented and assessed based on a wide range of earthquake input motions.

Visual retrieval of concrete crack properties for automated post-earthquake structural safety evaluation

The safety of post-earthquake structures is evaluated manually through inspecting the visible damage inflicted on structural elements. This process is time-consuming and costly. In order to automate this type of assessment, several crack detection methods have been created. However, they focus on locating crack points. The next step, retrieving useful properties (e.g. crack width, length, and orientation) from the crack points, has not yet been adequately investigated. This paper presents a novel method of retrieving crack properties. In the method, crack points are first located through state-of-the-art crack detection techniques. Then, the skeleton configurations of the points are identified using image thinning. The configurations are integrated into the distance field of crack points calculated through a distance transform. This way, crack width, length, and orientation can be automatically retrieved. The method was implemented using Microsoft Visual Studio and its effectiveness was tested on real crack images collected from Haiti.

Machine Vision Enhanced Post-Earthquake Inspection and Rapid Loss Estimation

Manual inspection is required to determine the condition of damaged buildings after an earthquake. The lack of available inspectors, when combined with the large volume of inspection work, makes such inspection subjective and time-consuming. Completing the required inspection takes weeks to complete, which has adverse economic and societal impacts on the affected population. This paper proposes an automated framework for rapid post-earthquake building evaluation. Under the framework, the visible damage (cracks and buckling) inflicted on concrete columns is first detected. The damage properties are then measured in relation to the column's dimensions and orientation, so that the column's load bearing capacity can be approximated as a damage index. The column damage index supplemented with other building information (e.g. structural type and columns arrangement) is then used to query fragility curves of similar buildings, constructed from the analyses of existing and on-going experimental data. The query estimates the probability of the building being in different damage states. The framework is expected to automate the collection of building damage data, to provide a quantitative assessment of the building damage state, and to estimate the vulnerability of the building to collapse in the event of an aftershock. Videos and manual assessments of structures after the 2009 earthquake in Haiti are used to test the parts of the framework.

Machine Vision Enhanced Post-earthquake Inspection

Camera motion estimation is one of the most significant steps for structure-from-motion (SFM) with a monocular camera. The normalized 8-point, the 7-point, and the 5-point algorithms are normally adopted to perform the estimation, each of which has distinct performance characteristics. Given unique needs and challenges associated to civil infrastructure SFM scenarios, selection of the proper algorithm directly impacts the structure reconstruction results. In this paper, a comparison study of the aforementioned algorithms is conducted to identify the most suitable algorithm, in terms of accuracy and reliability, for reconstructing civil infrastructure. The free variables tested are baseline, depth, and motion. A concrete girder bridge was selected as the "test-bed" to reconstruct using an off-the-shelf camera capturing imagery from all possible positions that maximally the bridge's features and geometry. The feature points in the images were extracted and matched via the SURF descriptor. Finally, camera motions are estimated based on the corresponding image points by applying the aforementioned algorithms, and the results evaluated.

Automated Detection of Exposed Reinforcement in Post-Earthquake Safety and Structural Evaluations

Post-earthquake structural safety evaluations are currently performed manually by a team of certified inspectors and/or structural engineers. This process is time-consuming and costly, keeping owners and occupants from returning to their businesses and homes. Automating these evaluations would enable faster, and potentially more consistent, relief and response processes. In order to do this, the detection of exposed reinforcing steel is of utmost significance. This paper presents a novel method of detecting exposed reinforcement in concrete columns for the purpose of advancing practices of structural and safety evaluation of buildings after earthquakes. Under this method, the binary image of the reinforcing area is first isolated using a state-of-the-art adaptive thresholding technique. Next, the ribbed regions of the reinforcement are detected by way of binary template matching. Finally, vertical and horizontal profiling are applied to the processed image in order to filter out any superfluous pixels and take into consideration the size of reinforcement bars in relation to that of the structural element within which they reside. The final result is the combined binary image disclosing only the regions containing rebar overlaid on top of the original image. The method is tested on a set of images from the January 2010 earthquake in Haiti. Preliminary test results convey that most exposed reinforcement could be properly detected in images of moderately-to-severely damaged concrete columns.