On systematic approaches for interpreted information transfer of inspection data from bridge models to structural analysis

In conjunction with the improved methods of monitoring damage and degradation processes, the interest in reliability assessment of reinforced concrete bridges is increasing in recent years. Automated imagebased inspections of the structural surface provide valuable data to extract quantitative information about deteriorations, such as crack patterns. However, the knowledge gain results from processing this information in a structural context, i.e. relating the damage artifacts to building components. This way, transformation to structural analysis is enabled. This approach sets two further requirements: availability of structural bridge information and a standardized storage for interoperability with subsequent analysis tools. Since the involved large datasets are only efficiently processed in an automated manner, the implementation of the complete workflow from damage and building data to structural analysis is targeted in this work. First, domain concepts are derived from the back-end tasks: structural analysis, damage modeling, and life-cycle assessment. The common interoperability format, the Industry Foundation Class (IFC), and processes in these domains are further assessed. The need for usercontrolled interpretation steps is identified and the developed prototype thus allows interaction at subsequent model stages. The latter has the advantage that interpretation steps can be individually separated into either a structural analysis or a damage information model or a combination of both. This approach to damage information processing from the perspective of structural analysis is then validated in different case studies

Context-Enabled Visualization Strategies for Automation Enabled Human-in-the-loop Inspection Systems to Enhance the Situation Awareness of Windstorm Risk Engineers

Insurance loss prevention survey, specifically windstorm risk inspection survey is the process of investigating potential damages associated with a building or structure in the event of an extreme weather condition such as a hurricane or tornado. Traditionally, the risk inspection process is highly subjective and depends on the skills of the engineer performing it. This dissertation investigates the sensemaking process of risk engineers while performing risk inspection with special focus on various factors influencing it. This research then investigates how context-based visualizations strategies enhance the situation awareness and performance of windstorm risk engineers. An initial study investigated the sensemaking process and situation awareness requirements of the windstorm risk engineers. The data frame theory of sensemaking was used as the framework to carry out this study. Ten windstorm risk engineers were interviewed, and the data collected were analyzed following an inductive thematic approach. The themes emerged from the data explained the sensemaking process of risk engineers, the process of making sense of contradicting information, importance of their experience level, internal and external biases influencing the inspection process, difficulty developing mental models, and potential technology interventions. More recently human in the loop systems such as drones have been used to improve the efficiency of windstorm risk inspection. This study provides recommendations to guide the design of such systems to support the sensemaking process and situation awareness of windstorm visual risk inspection. The second study investigated the effect of context-based visualization strategies to enhance the situation awareness of the windstorm risk engineers. More specifically, the study investigated how different types of information contribute towards the three levels of situation awareness. Following a between subjects study design 65 civil/construction engineering students completed this study. A checklist based and predictive display based decision aids were tested and found to be effective in supporting the situation awareness requirements as well as performance of windstorm risk engineers. However, the predictive display only helped with certain tasks like understanding the interaction among different components on the rooftop. For remaining tasks, checklist alone was sufficient. Moreover, the decision aids did not place any additional cognitive demand on the participants. This study helped us understand the advantages and disadvantages of the decision aids tested. The final study evaluated the transfer of training effect of the checklist and predictive display based decision aids. After one week of the previous study, participants completed a follow-up study without any decision aids. The performance and situation awareness of participants in the checklist and predictive display group did not change significantly from first trial to second trial. However, the performance and situation awareness of participants in the control condition improved significantly in the second trial. They attributed this to their exposure to SAGAT questionnaire in the first study. They knew what issues to look for and what tasks need to be completed in the simulation. The confounding effect of SAGAT questionnaires needs to be studied in future research efforts

For a learnable mathematics in the digital cultures

I begin with some general remarks concerning the co-evolution of representational forms and mathematical meanings. I then discuss the changed roles of mathematics and novel representations that emerge from the ubiquity of computational models, and briefly consider the implications for learning mathematics. I contend that a central component of knowledge required in modern societies involves the development of a meta-epistemological stance – i.e. developing a sense of mechanism for the models that underpin social and professional discourses. I illustrate this point in relation to recent research in which I am investigating the mathematical epistemology of engineering practice. Finally, I map out one implication for the design of future mathematical learning environments with reference to some data from the "Playground Project"

Guide to Life-Cycle Data and Information Sharing Workflows for Transportation Assets TR-714, September 2018

Thanks to an array of advanced digital technologies, much of today’s transportation project data are available in digital format. However, due to the fragmented nature of the highway project delivery process, the growing amount of digital data is being archived and managed separately. This makes it difficult for professionals to take full advantage of the efficiencies of digitized data and information. The purpose of this research was to identify current data workflows and areas for improvement for five of the most common types of highway assets—signs, guardrails, culverts, pavements, and bridges—and offer guidance to practitioners on how to better collect, manage, and exchange asset data. The research team conducted focus group discussions and interviews with highway professionals to capture their knowledge and practices about the data workflows. In addition, the team conducted an extensive review of the literature, manuals, project documents, and software applications regarding the exchanged information. For each type of asset, an information delivery manual (IDM) was developed. Each IDM consists of several process maps (PMs) and one exchange requirement (ER) matrix. A total of 15 PMs and 5 ER matrices were developed. A set of limitations in current data workflows was identified and a set of recommendations to overcome those limitations was also determined and documented. The conclusion was that current data workflows were designed mostly for contract administration purposes. Thus, more efficient asset-centric data workflows need to be implemented to truly streamline the data workflows throughout an asset’s life cycle and minimize wasted resources in recreating data in the asset maintenance stage

Building an ontological knowledgebase for bridge maintenance

The operation stage has the biggest potential value in the bridge life cycle management, and it often critically influences the overall cost of the bridge. As such, changes in the efficiency of the project's operation stage could be of significant benefit to the overall project. However, current approaches in the operation stage often lack the effective support of computer-aided tools. This research presents a holistic method based on an ontology to achieve automatic rule checking and improve the management and communication of knowledge related to bridge maintenance. The developed ontology can also facilitate a smarter decision-making process for bridge management by informing engineers of choices with different considerations. Three approaches; semantic validation, syntactical validation, and case study validation, have been adopted to evaluate this ontology and demonstrate how the developed ontology can be used by engineers when dealing with different issues. The results showed that this approach can create a holistic knowledge base that can integrate various domain knowledge to enable bridge engineers to make more comprehensive decisions rather than a single objective-targeted delivery

Guide to Life-Cycle Data and Information Sharing Workflows for Transportation Assets TR-714, September 2018

Thanks to an array of advanced digital technologies, much of today’s transportation project data are available in digital format. However, due to the fragmented nature of the highway project delivery process, the growing amount of digital data is being archived and managed separately. This makes it difficult for professionals to take full advantage of the efficiencies of digitized data and information. The purpose of this research was to identify current data workflows and areas for improvement for five of the most common types of highway assets—signs, guardrails, culverts, pavements, and bridges—and offer guidance to practitioners on how to better collect, manage, and exchange asset data. The research team conducted focus group discussions and interviews with highway professionals to capture their knowledge and practices about the data workflows. In addition, the team conducted an extensive review of the literature, manuals, project documents, and software applications regarding the exchanged information. For each type of asset, an information delivery manual (IDM) was developed. Each IDM consists of several process maps (PMs) and one exchange requirement (ER) matrix. A total of 15 PMs and 5 ER matrices were developed. A set of limitations in current data workflows was identified and a set of recommendations to overcome those limitations was also determined and documented. The conclusion was that current data workflows were designed mostly for contract administration purposes. Thus, more efficient asset-centric data workflows need to be implemented to truly streamline the data workflows throughout an asset’s life cycle and minimize wasted resources in recreating data in the asset maintenance stage

INSPIRE Newsletter Spring 2022

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