Seismic Performance and Design of Bridge Foundations in Liquefiable Ground with a Frozen Crust

INE/AUTC 12.3

Exergy-based Planning and Thermography-based Monitoring for energy efficient buildings - Progress Report (KIT Scientific Reports ; 7632)

Designing and monitoring energy efficiency of buildings is vital since they account for up to 40% of end-use energy. In this study, exergy analysis is investigated as a life cycle design tool to strike a balance between thermodynamic efficiency of energy conversion and economic and environmental costs of construction. Quantitative geo-referenced thermography is proposed for monitoring and quantitative assessment via continued simulation and parameter estimation during the operating phase

Automatic Change-based Diagnosis of Structures Using Spatiotemporal Data and As- Designed Model

abstract: Civil infrastructures undergo frequent spatial changes such as deviations between as-designed model and as-is condition, rigid body motions of the structure, and deformations of individual elements of the structure, etc. These spatial changes can occur during the design phase, the construction phase, or during the service life of a structure. Inability to accurately detect and analyze the impact of such changes may miss opportunities for early detections of pending structural integrity and stability issues. Commercial Building Information Modeling (BIM) tools could hardly track differences between as-designed and as-built conditions as they mainly focus on design changes and rely on project managers to manually update and analyze the impact of field changes on the project performance. Structural engineers collect detailed onsite data of a civil infrastructure to perform manual updates of the model for structural analysis, but such approach tends to become tedious and complicated while handling large civil infrastructures. Previous studies started collecting detailed geometric data generated by 3D laser scanners for defect detection and geometric change analysis of structures. However, previous studies have not yet systematically examined methods for exploring the correlation between the detected geometric changes and their relation to the behaviors of the structural system. Manually checking every possible loading combination leading to the observed geometric change is tedious and sometimes error-prone. The work presented in this dissertation develops a spatial change analysis framework that utilizes spatiotemporal data collected using 3D laser scanning technology and the as-designed models of the structures to automatically detect, classify, and correlate the spatial changes of a structure. The change detection part of the developed framework is computationally efficient and can automatically detect spatial changes between as-designed model and as-built data or between two sets of as-built data collected using 3D laser scanning technology. Then a spatial change classification algorithm automatically classifies the detected spatial changes as global (rigid body motion) and local deformations (tension, compression). Finally, a change correlation technique utilizes a qualitative shape-based reasoning approach for identifying correlated deformations of structure elements connected at joints that contradicts the joint equilibrium. Those contradicting deformations can help to eliminate improbable loading combinations therefore guiding the loading path analysis of the structure.Dissertation/ThesisDoctoral Dissertation Civil and Environmental Engineering 201

Integrated multiple-sensor methodology for condition assessment of water mains

Considerable capital investment has been made in civil infrastructure systems across the globe including water mains. Currently, existing failure detection and location methods do not allow for quick reaction to failures. In addition, no unified standards are followed in condition assessment of water mains. Moreover, the decision by which water mains are inspected is currently carried out based on approximation and experience of decision-makers, which might be limited and may lead to overlooking suitable evaluation methods that might save time, effort and cost. This research presents a methodology that contributes to different phases in the asset management of water mains. It enhances current practice in condition assessment of water mains and assists in setting up rehabilitation priorities. The methodology implemented is based on intensive literature review, evaluation of current methods, field investigation and experiments, and interviews with experts. The methodology considered augments two approaches currently used in condition assessment of water mains, which are proactive and reactive methods. The developed methodology calls for designing a new decision support system (DSS) for selection of most suitable non-destructive evaluation (NDE) method(s). It consists of two components: 1) A Database management system (DBMS), and 2) an evaluation and ranking module (ERM). These NDE methods are used for either detecting suspected leaks or measuring pipe wall thickness, the latter is employed to predict remaining service life of pipe being considered. In case of suspected leaks, this study presents a newly designed automated system for detection of water leaks in underground pipelines and identifying their respective locations. The development of this system is based on using Thermography infrared (IR) camera in order to detect thermal contrast at the pavement surface due to water leaks at the most suitable time. The data obtained is analyzed in order to establish the relationship between the detected leakage area and the approximate location of leak. Prototype software developed in Visual C# environment is implemented in order to determine the location of leaks automatically. Two deterioration models were designed and developed for estimating remaining useful life of water mains, and predicting annual breakage rate of water mains. The development of the two models is based on the analysis of real data collected from 16 municipalities in Canada and the US. The two models were developed considering two approaches, multiple regression analysis and Artificial Neural Networks (ANNs) based on the most suitable subsets of selected factors. The final model was selected due to its reliability and better performance in comparison to other models. The outputs of the deterioration models developed in this research were used, in addition to other deterioration factors that were not considered in existing models in order to develop a Decision Support System (DSS) for generating condition rating scale of water main being considered, and for prioritizing rehabilitation/ maintenance actions. The system is hierarchal in structure, and the condition-rating index is generated using Multi Attributes Utility Theory (MAUT). System validation was carried out by comparing its outcome with real case studies. A prototype software application of the model presented in this research is implemented as a proof of concept to demonstrate the capabilities and essential features of the developed models

Damage and repair identification in reinforced concrete beams modelled with various damage scenarios using vibration data

This research aims at developing a novel vibration-based damage identification technique that can efficiently be applied to real-time large data for detection, classification, localisation and quantification of the potential structural damage

Defining and Evaluating Test Suite Consolidation for Event Sequence-based Test Cases

This research presents a new test suite consolidation technique, called CONTEST, for automated GUI testing. A new probabilistic model of the GUI is developed to allow direct application of CONTEST. Multiple existing test suites are used to populate the model and compute probabilities based on the observed event sequences. These probabilities are used to generate a new test suite that consolidates the original ones. A new test suite similarity metric, called CONTeSSi(n), is introduced which compares multiple event sequence-based test suites using relative event positions. Results of empirical studies showed that CONTEST yields a test suite that achieves better fault detection and code coverage than the original suites, and that the CONTeSSi(n) metric is a better indicator of the similarity between sequence-based test suites than existing metrics