Human inspection is a pivotal component of infrastructure asset management within a systems thinking approach to civil engineering. Skilled inspectors are tasked with the evaluation of various civil infrastructure components, conducting assessments of their conditions, identifying maintenance needs, and determining necessary repairs. Despite the growing interest in advanced technologies and automated inspections, the use of human-in-the-loop procedures is still widely practiced. Humans are susceptible to cognitive bias, variability, or uncertainty when inspecting infrastructure, and finding solutions to reduce these factors is paramount.
This study presents a comprehensive exploration of inspection variability within infrastructure asset management, drawing insights from datasets of the BUILDER Sustainment Management System (SMS) program. The research delves into infrastructure inventory, inspector data, and inspection data components of an asset management database, shedding light on variability in human inspection. Variations in inspection ratings revealed significant concerns, particularly in Mechanical, Electrical, and Plumbing (MEP) systems, with notable disparities between inspection ratings and condition ratings. Inspector variability analysis, through Coefficient of Variation calculations, indicated substantial disparities within and among inspectors. Further analysis, including Tukey's HSD test, pinpointed significant variability in heating, ventilation, and air conditioning (HVAC) and Fire Protection system inspections.
Moreover, this study addresses the specific challenge of reducing inspection uncertainty in HVAC systems. HVAC systems play a critical role in facility energy consumption, and their maintenance is vital to energy efficiency and occupant comfort. However, HVAC-specific inspections primarily require human involvement, making them time-consuming and prone to error. Addressing the challenges surrounding human inspection of HVAC systems, this research presents a multifaceted approach to reduce variability. Drawing from a review of existing literature on HVAC inspection uncertainty, this study extends its focus to the development of predictive models. These models considered parameters including inspection ratings, age-based obsolescence, section condition indices, component characteristics, and unique inspectors . Utilizing Linear Regression, Random Forest, and Gradient Boosting Regression, this model accurately predicted Variability Ratings, signifying the potential for implementation as a decision support tool. Importantly, the findings highlight the need to not only understand the factors affecting HVAC inspection variability but to actively implement technological solutions that can reduce human error and variability in inspections. / Master of Science / Infrastructure inspection is crucial for maintaining buildings and facilities, but it often comes with human errors and uncertainties. This study looks at the inspection process, focusing on case studies and data from the BUILDER Sustainment Management System (SMS) program. It reveals that inspectors sometimes evaluate the condition of parts of a building differently, leading to inconsistencies and poor overall management.
One significant area of concern is heating, ventilation, and air conditioning (HVAC) systems. These systems play a critical role in facility energy use and can be challenging to inspect accurately. Previous research has shown that work experience, training, education, and other factors tend to contribute to variability in how inspectors assess HVAC systems.
This research not only highlights these issues but also develops predictive models to reduce the variability of HVAC inspections. By doing so infrastructure can be managed correctly and ultimately lead to improved building lifecycles.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/117309 |
| Date | 05 January 2024 |
| Creators | Pratt, Clayton Michael |
| Contributors | Civil and Environmental Engineering, Shealy, Earl Wade, Jazizadeh Karimi, Farrokh, Garvin, Michael J. |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | ETD, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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