Matching Items (7)
Filtering by
- All Subjects: Civil Engineering
- Genre: Doctoral Dissertation
- Creators: Tang, Pingbo
- Resource Type: Text
- Status: Published
Description
Implementing Building Information Modeling (BIM) in construction projects has many potential benefits, but issues of projects can hinder its realization in practice. Although BIM involves using the technology, more than four-fifths of the recurring issues in current BIM-based construction projects are related to the people and processes (i.e., the non-technological elements of BIM). Therefore, in addition to the technological skills required for using BIM, educators should also prepare university graduates with the non-technological skills required for managing the people and processes of BIM. This research’s objective is to develop a learning module that teaches the non-technological skills for addressing common, people- and process-related, issues in BIM-based construction projects. To achieve this objective, this research outlines the steps taken to create the learning module and identify its impact on a BIM course. The contribution of this research is in the understanding of the pedagogical value of the developed problem-based learning module and documenting the learning module’s development process.
ContributorsAbdul Rahman, Abdul Rahimi Bin (Author) / Ayer, Steven K (Thesis advisor) / Tang, Pingbo (Committee member) / Wiezel, Avi (Committee member) / Arizona State University (Publisher)
Created2018
Description
The United States building sector was the most significant carbon emission contributor (over 40%). The United States government is trying to decrease carbon emissions by enacting policies, but emissions increased by approximately 7 percent in the U.S. between 1990 and 2013. To reduce emissions, investigating the factors affecting carbon emissions should be a priority. Therefore, in this dissertation, this research examine the relationship between carbon emissions and the factors affecting them from macro and micro perspectives. From a macroscopic perspective, the relationship between carbon dioxide, energy resource consumption, energy prices, GDP (gross domestic product), waste generation, and recycling waste generation in the building and waste sectors has been verified. From a microscopic perspective, the impact of non-permanent electric appliances and stationary and non-stationary occupancy has been investigated. To verify the relationships, various kinds of statistical and data mining techniques were applied, such as the Granger causality test, linear and logarithmic correlation, and regression method. The results show that natural gas and electricity prices are higher than others, as coal impacts their consumption, and electricity and coal consumption were found to cause significant carbon emissions. Also, waste generation and recycling significantly increase and decrease emissions from the waste sector, respectively. Moreover, non-permanent appliances such as desktop computers and monitors consume a lot of electricity, and significant energy saving potential has been shown. Lastly, a linear relationship exists between buildings’ electricity use and total occupancy, but no significant relationship exists between occupancy and thermal loads, such as cooling and heating loads. These findings will potentially provide policymakers with a better understanding of and insights into carbon emission manipulation in the building sector.
ContributorsLee, Seungtaek (Author) / Chong, Oswald (Thesis advisor) / Sullivan, Kenneth (Committee member) / Tang, Pingbo (Committee member) / Arizona State University (Publisher)
Created2018
Description
Resilient acquisition of timely, detailed job site information plays a pivotal role in maintaining the productivity and safety of construction projects that have busy schedules, dynamic workspaces, and unexpected events. In the field, construction information acquisition often involves three types of activities including sensor-based inspection, manual inspection, and communication. Human interventions play critical roles in these three types of field information acquisition activities. A resilient information acquisition system is needed for safer and more productive construction. The use of various automation technologies could help improve human performance by proactively providing the needed knowledge of using equipment, improve the situation awareness in multi-person collaborations, and reduce the mental workload of operators and inspectors.
Unfortunately, limited studies consider human factors in automation techniques for construction field information acquisition. Fully utilization of the automation techniques requires a systematical synthesis of the interactions between human, tasks, and construction workspace to reduce the complexity of information acquisition tasks so that human can finish these tasks with reliability. Overall, such a synthesis of human factors in field data collection and analysis is paving the path towards “Human-Centered Automation” (HCA) in construction management. HCA could form a computational framework that supports resilient field data collection considering human factors and unexpected events on dynamic job sites.
This dissertation presented an HCA framework for resilient construction field information acquisition and results of examining three HCA approaches that support three use cases of construction field data collection and analysis. The first HCA approach is an automated data collection planning method that can assist 3D laser scan planning of construction inspectors to achieve comprehensive and efficient data collection. The second HCA approach is a Bayesian model-based approach that automatically aggregates the common sense of people from the internet to identify job site risks from a large number of job site pictures. The third HCA approach is an automatic communication protocol optimization approach that maximizes the team situation awareness of construction workers and leads to the early detection of workflow delays and critical path changes. Data collection and simulation experiments extensively validate these three HCA approaches.
Unfortunately, limited studies consider human factors in automation techniques for construction field information acquisition. Fully utilization of the automation techniques requires a systematical synthesis of the interactions between human, tasks, and construction workspace to reduce the complexity of information acquisition tasks so that human can finish these tasks with reliability. Overall, such a synthesis of human factors in field data collection and analysis is paving the path towards “Human-Centered Automation” (HCA) in construction management. HCA could form a computational framework that supports resilient field data collection considering human factors and unexpected events on dynamic job sites.
This dissertation presented an HCA framework for resilient construction field information acquisition and results of examining three HCA approaches that support three use cases of construction field data collection and analysis. The first HCA approach is an automated data collection planning method that can assist 3D laser scan planning of construction inspectors to achieve comprehensive and efficient data collection. The second HCA approach is a Bayesian model-based approach that automatically aggregates the common sense of people from the internet to identify job site risks from a large number of job site pictures. The third HCA approach is an automatic communication protocol optimization approach that maximizes the team situation awareness of construction workers and leads to the early detection of workflow delays and critical path changes. Data collection and simulation experiments extensively validate these three HCA approaches.
ContributorsZhang, Cheng (Author) / Tang, Pingbo (Thesis advisor) / Cooke, Nancy J. (Committee member) / Chong, Oswald (Committee member) / Arizona State University (Publisher)
Created2017
Description
Emerging information and communication technology (ICT) has had an enormous effect on the building architecture, engineering, construction and operation (AECO) fields in recent decades. The effects have resonated in several disciplines, such as project information flow, design representation and communication, and Building Information Modeling (BIM) approaches. However, these effects can potentially impact communication and coordination of the virtual design contents in both design and construction phases. Therefore, and with the great potential for emerging technologies in construction projects, it is essential to understand how these technologies influence virtual design information within the organizations as well as individuals’ behaviors. This research focusses on understanding current emerging technologies and its impacts on projects virtual design information and communication among projects stakeholders within the AECO organizations.
ContributorsAlsafouri, Suleiman (Author) / Ayer, Steven (Thesis advisor) / Tang, Pingbo (Committee member) / Atkinson, Robert (Committee member) / Arizona State University (Publisher)
Created2017
Description
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.
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.
ContributorsKalasapudi, Vamsi Sai (Author) / Tang, Pingbo (Thesis advisor) / Chong, Oswald (Committee member) / Hjelmstad, Keith (Committee member) / Arizona State University (Publisher)
Created2017
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
Description
Interpersonal communications during civil infrastructure systems operation and maintenance (CIS O&M) are processes for CIS O&M participants to exchange critical information. Poor communications that provide misleading information can jeopardize CIS O&M safety and efficiency. Previous studies suggest that communication contexts and features could be indicators of communication errors and relevant CIS O&M risks. However, challenges remain for reliable prediction of communication errors to ensure CIS O&M safety and efficiency. For example, existing studies lack a systematic summarization of risky contexts and features of communication processes for predicting communication errors. Limited studies examined quantitative methods for incorporating expert opinions as constraints for reliable communication error prediction. How to examine mitigation strategies (e.g., adjustments of communication protocols) for reducing communication-related CIS O&M risks is also challenging. The main reason is the lack of causal analysis about how various factors influence the occurrences and impacts of communication errors so that engineers lack the basis for intervention.
This dissertation presents a method that integrates Bayesian Network (BN) modeling and simulation for communication-related risk prediction and mitigation. The proposed method aims at tackling the three challenges mentioned above for ensuring CIS O&M safety and efficiency. The proposed method contains three parts: 1) Communication Data Collection and Error Detection – designing lab experiments for collecting communication data in CIS O&M workflows and using the collected data for identifying risky communication contexts and features; 2) Communication Error Classification and Prediction – encoding expert knowledge as constraints through BN model updating to improve the accuracy of communication error prediction based on given communication contexts and features, and 3) Communication Risk Mitigation – carrying out simulations to adjust communication protocols for reducing communication-related CIS O&M risks.
This dissertation uses two CIS O&M case studies (air traffic control and NPP outages) to validate the proposed method. The results indicate that the proposed method can 1) identify risky communication contexts and features, 2) predict communication errors and CIS O&M risks, and 3) reduce CIS O&M risks triggered by communication errors. The author envisions that the proposed method will shed light on achieving predictive control of interpersonal communications in dynamic and complex CIS O&M.
This dissertation presents a method that integrates Bayesian Network (BN) modeling and simulation for communication-related risk prediction and mitigation. The proposed method aims at tackling the three challenges mentioned above for ensuring CIS O&M safety and efficiency. The proposed method contains three parts: 1) Communication Data Collection and Error Detection – designing lab experiments for collecting communication data in CIS O&M workflows and using the collected data for identifying risky communication contexts and features; 2) Communication Error Classification and Prediction – encoding expert knowledge as constraints through BN model updating to improve the accuracy of communication error prediction based on given communication contexts and features, and 3) Communication Risk Mitigation – carrying out simulations to adjust communication protocols for reducing communication-related CIS O&M risks.
This dissertation uses two CIS O&M case studies (air traffic control and NPP outages) to validate the proposed method. The results indicate that the proposed method can 1) identify risky communication contexts and features, 2) predict communication errors and CIS O&M risks, and 3) reduce CIS O&M risks triggered by communication errors. The author envisions that the proposed method will shed light on achieving predictive control of interpersonal communications in dynamic and complex CIS O&M.
ContributorsSun, Zhe (Author) / Tang, Pingbo (Thesis advisor) / Ayer, Steven K (Committee member) / Cooke, Nancy J. (Committee member) / Liu, Yongming (Committee member) / Arizona State University (Publisher)
Created2020