Matching Items (3)
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- All Subjects: Compliance
- All Subjects: civil infrastructure maintenance
- Creators: Ayer, Steven
Description
Since its launch by the US Green Building Council (USGBC), Leadership in Energy and Environmental Design (LEED) certification has been postured as the "gold standard" for environmentally conscious, sustainable building design, construction and operations. However, as a "living measurement", one which requires ongoing evaluation and reporting of attainment and compliance with LEED certification requirements, there is none. Once awarded, LEED certification does not have a required reporting component to effectively track continued adherence to LEED standards. In addition, there is no expiry tied to the certification; once obtained, a LEED certification rating is presumed to be a valid representation of project certification status. Therefore, LEED lacks a requirement to demonstrate environmental impact of construction materials and building systems over the entire life of the project. Consequently, LEED certification is merely a label rather than a true representation of ongoing adherence to program performance requirements over time. Without continued monitoring and reporting of building design and construction features, and in the absence of recertification requirements, LEED is, in reality, a gold star rather than a gold standard. This thesis examines the lack of required ongoing monitoring, reporting, or recertification requirements following the award by the USGBC of LEED certification; compares LEED with other international programs which do have ongoing reporting or recertification requirements; demonstrates the need and benefit of ongoing reporting or recertification requirements; and explores possible methods for implementation of mandatory reporting requirements within the program.
ContributorsCarpenter, Anne Therese (Author) / Olson, Larry (Thesis advisor) / Hild, Nicholas (Committee member) / Brown, Albert (Committee member) / Arizona State University (Publisher)
Created2013
Description
As construction and building methods advance so should their focus on reconstruction post-natural disasters. For the past 50 years there has been an average of 6.2 hurricanes making landfall, and several recent unfortunate occurrences in the past year that have caused immeasurable damage and taken priceless lives (Chris Landsea 2017). Damages could have been significantly reduced to residential homes and lives saved if proper, hurricane-resistant construction was used. It is important to continue advancement in efficient planning and reconstructive methods to restore individuals into their homes and ensure their safety in the future. Utilizing tested resilient building methods may increase construction costs but has a visible payoff through mitigation of economic losses in the future. This can also help develop response and mitigation plans based on the very specific conditions of each community or affected location. To do so, it is crucial to continue research and test various methods of construction and materials in residential homes. This study was a comparative analysis of the current roof systems implemented in residential homes, the role of hurricane testing facilities in maintaining building codes, and how damage incurred by hurricanes can be significantly reduced through a shift in the approach of homeowner insurance incentive. The purpose of this study was to provide a feasible and practicable solution for increasing implementation of hurricane resistant construction into homes. The results of this analysis concluded that there is a low percentage of homeowners investing in making their homes hurricane resilient. By re-inventing the incentive methods that insurance companies offer, this problem can step into the right direction in making more homes hurricane resilient consequently reducing damages, deaths, and economic loss.
ContributorsVarkalaite, Migle (Author) / Sullivan, Kenneth (Thesis director) / Ayer, Steven (Committee member) / School of International Letters and Cultures (Contributor) / Del E. Webb Construction (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Imagery data has become important for civil infrastructure operation and
maintenance because imagery data can capture detailed visual information with high
frequencies. Computer vision can be useful for acquiring spatiotemporal details to
support the timely maintenance of critical civil infrastructures that serve society. Some
examples include: irrigation canals need to maintain the leaking sections to avoid water
loss; project engineers need to identify the deviating parts of the workflow to have the
project finished on time and within budget; detecting abnormal behaviors of air traffic
controllers is necessary to reduce operational errors and avoid air traffic accidents.
Identifying the outliers of the civil infrastructure can help engineers focus on targeted
areas. However, large amounts of imagery data bring the difficulty of information
overloading. Anomaly detection combined with contextual knowledge could help address
such information overloading to support the operation and maintenance of civil
infrastructures.
Some challenges make such identification of anomalies difficult. The first challenge is
that diverse large civil infrastructures span among various geospatial environments so
that previous algorithms cannot handle anomaly detection of civil infrastructures in
different environments. The second challenge is that the crowded and rapidly changing
workspaces can cause difficulties for the reliable detection of deviating parts of the
workflow. The third challenge is that limited studies examined how to detect abnormal
behaviors for diverse people in a real-time and non-intrusive manner. Using video andii
relevant data sources (e.g., biometric and communication data) could be promising but
still need a baseline of normal behaviors for outlier detection.
This dissertation presents an anomaly detection framework that uses contextual
knowledge, contextual information, and contextual data for filtering visual information
extracted by computer vision techniques (ADCV) to address the challenges described
above. The framework categorizes the anomaly detection of civil infrastructures into two
categories: with and without a baseline of normal events. The author uses three case
studies to illustrate how the developed approaches can address ADCV challenges in
different categories of anomaly detection. Detailed data collection and experiments
validate the developed ADCV approaches.
maintenance because imagery data can capture detailed visual information with high
frequencies. Computer vision can be useful for acquiring spatiotemporal details to
support the timely maintenance of critical civil infrastructures that serve society. Some
examples include: irrigation canals need to maintain the leaking sections to avoid water
loss; project engineers need to identify the deviating parts of the workflow to have the
project finished on time and within budget; detecting abnormal behaviors of air traffic
controllers is necessary to reduce operational errors and avoid air traffic accidents.
Identifying the outliers of the civil infrastructure can help engineers focus on targeted
areas. However, large amounts of imagery data bring the difficulty of information
overloading. Anomaly detection combined with contextual knowledge could help address
such information overloading to support the operation and maintenance of civil
infrastructures.
Some challenges make such identification of anomalies difficult. The first challenge is
that diverse large civil infrastructures span among various geospatial environments so
that previous algorithms cannot handle anomaly detection of civil infrastructures in
different environments. The second challenge is that the crowded and rapidly changing
workspaces can cause difficulties for the reliable detection of deviating parts of the
workflow. The third challenge is that limited studies examined how to detect abnormal
behaviors for diverse people in a real-time and non-intrusive manner. Using video andii
relevant data sources (e.g., biometric and communication data) could be promising but
still need a baseline of normal behaviors for outlier detection.
This dissertation presents an anomaly detection framework that uses contextual
knowledge, contextual information, and contextual data for filtering visual information
extracted by computer vision techniques (ADCV) to address the challenges described
above. The framework categorizes the anomaly detection of civil infrastructures into two
categories: with and without a baseline of normal events. The author uses three case
studies to illustrate how the developed approaches can address ADCV challenges in
different categories of anomaly detection. Detailed data collection and experiments
validate the developed ADCV approaches.
ContributorsChen, Jiawei (Author) / Tang, Pingbo (Thesis advisor) / Ayer, Steven (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2020