Browsing by Subject "Risk assessment"

Now showing 1 - 6 of 6

Blockchain-based methodology for confidential and traceable collaborative risk assessment
(2021-05-07) Chung, In Bae; Caldas, Carlos H.
Risk assessment is an important part of risk management in construction projects as it involves the identification, evaluation, and analysis of potential risk events, enabling project teams to properly mitigate them. To receive risk input from multiple stakeholders and aggregate them, different methodologies have been developed to support collaborative risk assessment. However, these systems do not ensure confidentiality in individual assessments, making it difficult for the stakeholders to provide their honest input. More problems such as lack of transparency and traceability occur as these systems are implemented in a centralized matter. Blockchain is an emergent decentralized digital technology that can provide solutions to such problems found in centralized systems. In this study, the methodology for collaborative risk assessment utilizing private data has been established based on the literature review of risk management tools and blockchain technology. A private permissioned blockchain network has been configured on Hyperledger Fabric which incorporates stakeholders for risk assessment, and smart contract algorithms have been developed to test out the functions explained in the methodology. Each step of the collaborative risk assessment process has been demonstrated on the prototype blockchain-based system. This research illustrates the benefits of adopting blockchain technology for collaborative risk assessment in construction projects and recommends future work based on its limitations.
Can strategic reasoning prompts improve auditors' sensitivity to fraud risk?
(2008-08) Bowlin, Kendall Owen; Kachelmeier, Steven J. (Steven John), 1958-
The basic premise of risk-based auditing is that more (fewer) audit resources should be allocated to accounts that are more (less) likely to be misstated. However, financial reporting managers can exploit such allocations by intentionally misstating balances that are less likely to draw auditor attention. If auditors do not recognize this strategic implication of risk-based auditing, undetected misstatements among ostensibly low-risk accounts could be much more common than traditional risk assessment procedures suggest. The purpose of this study is to examine whether prompting auditors to form beliefs about managers’ expectations of, and responses to, audit strategies can enhance auditors’ sensitivity to the strategic risk of fraud among accounts typically considered low-risk. Using a multi-account audit game, I find that auditors do not naturally attune to strategic risks but instead tend to focus resources on “highrisk” accounts. However, when auditors are prompted to reason strategically, they utilize more resources and devote that increase almost entirely to “low-risk” accounts. I also find that, although increasing available resources does result in an overall increase in the amount of utilized resources, the relative effect of the strategic prompt is robust to the level of available audit resources.
International project risk assessment
(2005) Walewski, John; Gibson, G. Edward (George Edward), 1958-
International construction projects are managed most effectively by planning for and addressing the risks that occur to all participants across the project’s entire life cycle. The first step in this process is the identification and assessment of such risks; however, there are few tools that provide such assistance. This research was undertaken to produce a user-friendly, systematic management tool to identify and assess the risks specific to international construction with the ultimate goal of improving project performance. This dissertation presents the development of the International Project Risk Assessment (IPRA) management tool including the methodology to create it, an analysis of its effectiveness in determining the relative importance of the identified risks, and the steps necessary to document, track, and mitigate international project-specific risks. The IPRA tool consists of 82 risk elements that are assessed by likelihood of occurrence and relative impact to identify those elements having the greatest potential impact on the project. Baseline Relative Impact values were developed for each of the 82 based on input from industry experts reporting on recently completed projects. The IPRA tool was tested on projects to verify its completeness and to assess the relationship of test and workshop relative impact values. Project performance data on test projects was collected to identify the relationship between risk and performance. A standardized case-study format was developed to identify which IPRA elements had the most impact on project performance. These results show that the IPRA tool is a sound method to identify and assess the relative impact of international risk issues. Nonetheless, this research also reveals that there is no single blueprint that adequately captures all the risks associated with every international project. Therefore, use of the IPRA tool must be tailored to adjust for country, user, and business sector concerns. Finally, although this research was limited by the paucity of empirical data on risk in international construction projects, the IPRA may provide a framework for the future collection and organization of such data.
Machine-learning-based models, methods, and software for intensity, vulnerability, and risk assessment of Central U.S. induced earthquakes
(2020-08-14) Khosravikia, Farid; Clayton, Patricia M.; Rathje, Ellen M.; Williamson, Eric B.; Zhang, Zhanmin; Faust, Kasey M.; Zhang, Ming
Since 2009, the Central U.S. has been subjected to a new type of seismic hazard attributed to human activities from the petroleum industry. Since then, there has been an increase in the number of earthquakes in the Central U.S. from an average of 25 per year in 2008 to 365 in 2017. These earthquakes can adversely affect the safety of infrastructure in the region, considering most were designed with minimal to no seismic detailing considerations due to the historically low seismicity in the region. The main objective of this dissertation is threefold: 1) To characterize the seismic demand of these earthquakes by developing region-specific ground motion models. 2) To evaluate the vulnerability of the built environment (in particular, bridge portfolios and residential buildings with masonry façades) to these recent earthquakes by developing fragility functions. 3) To integrate the ground motion and fragility models with other region-specific information to investigate regional consequences (i.e., potential economic loss) on the built environment for future seismic events. This information is now used by the Texas Department of Transportation to inform decision-making in terms of post-earthquake response and planning for future events. For each objective, the present study combines machine learning science with structural and earthquake engineering knowledge into a data-driven, state-of-the-art framework to develop more reliable prediction models compared to the conventional methods in the literature. This dissertation comparatively investigates the advantages of using machine learning techniques instead of conventional methods in developing each model (i.e., ground motion and fragility models). Moreover, this study investigates the seismic characteristics, vulnerability, and risk associated with these earthquakes, compared with those associated with other seismic hazards in the U.S. The comparison includes similar magnitude natural earthquakes in the Western U.S., New Madrid seismic hazards (i.e., the historical seismic hazard of interest in the Central U.S.), and estimates from HAZUS (i.e., the software provided by Federal Emergency Management Agency for disaster risk assessment). As part of this study, open-source application software named ShakeRisk is developed for risk, reliability, and resilience assessment of the built environment to natural hazards. ShakeRisk provides a platform to integrate artificial intelligence, systems engineering, structural and earthquake engineering research fields to simulate civil infrastructure responses at both structural and system scales in a reliable and computationally efficient way. Adopting clean architecture principles and object-oriented programming language in the design of ShakeRisk, it can be readily extended by adding features (i.e., new data sources, models, analyses, and user interfaces) and customizing existing ones without the need to modify existing code.
Modeling the post shear failure behavior of reinforced concrete columns
(2012-05) LeBorgne, Matthew Ronald; Ghannoum, Wassim M.; Wood, Sharon L.; Aggarwal, J K.; Bayrak, Oguzhan; Jirsa, James O.
Numerous reinforced concrete buildings vulnerable to earthquake induced collapse have been constructed in seismic zones prior to the 1970s. A major contributor to building collapse is the loss of axial load carrying capacity in non-seismically detailed columns. Experimental investigations have shown that non-seismically detailed columns will only experience axial failure after shear failure and subsequent lateral shear strength degradation have occurred. Therefore, column shear failure and degrading behavior must be modeled accurately before axial collapse algorithms can be properly implemented. Furthermore, accurate modeling of the degrading lateral-load behavior of columns is needed if lateral load sharing between structural elements is to be assessed with reasonable accuracy during seismic analyses. A calibrated analytical model was developed that is capable of estimating the lateral strength degrading behavior of RC columns prone to shear failure. Existing analytical models poorly approximate nonlinear column behavior and require several nonphysical damage parameters to be defined. In contrast, the proposed calibrated model provides the engineering community with a valuable tool that only requires the input of column material and geometric properties to simulate column behavior up to loss of lateral strength. In developing the model, a database of RC columns was compiled. Parameters extracted from database column-tests were scrutinized for trends and regression models relating damage parameters to column physical properties and boundary conditions were produced. The regression models were implemented in the degrading analytical framework that was developed in this project. Two reinforced concrete columns exhibiting significant inelastic deformations prior to failing in shear were tested in support of the analytical work. A newly developed Vision System was used to track a grid of targets on the column face with a resolution of three-thousands of an inch. Surface column deformations were measured to further the understanding of the fundamental changes in column behavior that accompany shear and axial failure and validate the proposed analytical model. This research provides the engineering community with an analytical tool that can be used to perform nonlinear dynamic analysis of buildings that are at risk of collapse and help engineers improve retrofit techniques. Further insight into shear behavior attained through this project is an important step toward the development of better shear and axial degradation models for reinforced concrete columns.
Resilience through risk assessment : a conceptual framework for extreme weather risk assessment of the Texas port system
(2021-05-06) Bathgate, Kyle Duram; Zhang, Zhanmin, 1962-
As extreme weather events increase in frequency and intensity, it is imperative to understand the existing resilience capabilities of critical physical infrastructure systems and identify areas for future improvement. Seaport systems are host to several interconnected and interdependent critical systems with a diverse set of freight transportation modes and supporting infrastructure systems present. Disruptions to port operations may have severe consequences for both local and regional economies. Therefore, understanding which infrastructure components are critical, vulnerable, and exposed to extreme weather hazards is paramount for identifying assets in need of retrofitting to reduce the risk of failure and increase system resilience. This study presents a conceptual framework to assess the risk of port system assets to extreme weather hazards. A review of relevant literature is presented, followed by the characterization of extreme weather events, a description of Texas port systems and inventory data, and methods and results of port stakeholder outreach activities. Finally, an initial conceptual framework is described that incorporates data and results obtained from previous chapters. The framework is intended to be straightforward for easy implementation and is targeted toward real-world application by port stakeholders. The framework contains four main components: 1) criticality assessment, 2) vulnerability assessment, 3) extreme weather exposure assessment, and 4) physical risk assessment. The risk scores may help inform port stakeholders as to the susceptibility of their infrastructure assets and may offer a tool to increase system resilience through future project selection and allocation measures.