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Youngjib Ham
Department of Construction Science, Texas A&M University, College Station, TX 77843, USA

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Journal article
Published: 21 August 2021 in Sustainability
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Construction disputes are one of the main challenges to successful construction projects. Most construction parties experience claims—and even worse, disputes—which are costly and time-consuming to resolve. Lessons learned from past failure cases can help reduce potential future risk factors that likely lead to disputes. In particular, case law, which has been accumulated from the past, is valuable information, providing useful insights to prepare for future disputes. However, few efforts have been made to discover legal knowledge using a large scale of case laws in the construction field. The aim of this paper is to enhance understanding of the multifaceted legal issues surrounding construction adjudication using large amounts of accumulated construction legal cases. This goal is achieved by exploring dispute-related contract terms and conditions that affect judicial decisions based on their verdicts. This study builds on text mining methods to examine what type of contract conditions are frequently referenced in the final decision of each dispute. Various text mining techniques are leveraged for knowledge discovery (i.e., analyzing frequent terms, discovering pairwise correlations, and identifying potential topics) in text-heavy data. The findings show that (1) similar patterns of disputes have occurred repeatedly in construction-related legal cases and (2) the discovered dispute topics indicate that mutually agreed upon contract terms and conditions are import in dispute resolution.

ACS Style

Jeehee Lee; Youngjib Ham; June-Seong Yi. Construction Disputes and Associated Contractual Knowledge Discovery Using Unstructured Text-Heavy Data: Legal Cases in the United Kingdom. Sustainability 2021, 13, 9403 .

AMA Style

Jeehee Lee, Youngjib Ham, June-Seong Yi. Construction Disputes and Associated Contractual Knowledge Discovery Using Unstructured Text-Heavy Data: Legal Cases in the United Kingdom. Sustainability. 2021; 13 (16):9403.

Chicago/Turabian Style

Jeehee Lee; Youngjib Ham; June-Seong Yi. 2021. "Construction Disputes and Associated Contractual Knowledge Discovery Using Unstructured Text-Heavy Data: Legal Cases in the United Kingdom." Sustainability 13, no. 16: 9403.

Journal article
Published: 01 January 2021 in Journal of Construction Engineering and Management
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Aging infrastructure has become a safety issue for local communities. For example, aging and deteriorating utility poles in strong winds have a high risk of falling onto roads, adjacent houses, or vehicles, which could cause traffic jams, power outages, property damage, or casualties. To prevent such accidents, the infrastructure condition needs to be monitored on a regular basis, thereby facilitating proactive maintenance and repair. However, available monitoring resources are limited for inspecting numerous existing infrastructures in a timely manner, which hinders obtaining up-to-date records representing the current condition status. As an alternative monitoring method, participatory sensing has the potential for infrastructure inspection, leveraging the prevalence of citizens’ smartphones as a ubiquitous sensing device. Nonetheless, the reliability of crowdsourced data for infrastructure monitoring and how to improve it have not been investigated fully, although participatory sensing increasingly has been adopted in many studies. Because citizens generally do not have expertise in infrastructure condition assessment, a lack of understanding of the crowdsourced data reliability prevents participatory sensing from being implemented in practice. To advance the understanding of the crowdsourced data reliability for infrastructure assessment, this study investigated the discrepancy in infrastructure assessment results by citizens and by an expert. Wood utility poles were selected as a target infrastructure. This study investigated a way to reduce the deviation between the expert’s and the citizens’ responses through a fuzzy inference system along with particle swarm optimization and pattern search algorithms. The experimental results showed that the proposed fuzzy inference system reduced the evaluation error by 21.18%. The findings of this study have the potential to fill the knowledge gap for enhancing participatory sensing in infrastructure monitoring, thereby promoting future study to enhance its applicability for citizen-driven urban resilience.

ACS Style

Hongjo Kim; Youngjib Ham. Increasing Reliability of Participatory Sensing for Utility Pole Condition Assessment Using Fuzzy Inference. Journal of Construction Engineering and Management 2021, 147, 04020154 .

AMA Style

Hongjo Kim, Youngjib Ham. Increasing Reliability of Participatory Sensing for Utility Pole Condition Assessment Using Fuzzy Inference. Journal of Construction Engineering and Management. 2021; 147 (1):04020154.

Chicago/Turabian Style

Hongjo Kim; Youngjib Ham. 2021. "Increasing Reliability of Participatory Sensing for Utility Pole Condition Assessment Using Fuzzy Inference." Journal of Construction Engineering and Management 147, no. 1: 04020154.

Original articles
Published: 14 November 2020 in Building Research & Information
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As one of the representative parameters for human energy metabolism, the metabolic rate has been considered as the significant factor for occupants’ thermal comfort analyses. Despite the importance of metabolic rate as a predictor of thermal comfort modelling, prior works rely on uncertain metabolic rate estimation without considering actual activity variations while occupying a building. This study aims at identifying the effect of metabolic rate on the thermal comfort models by proposing a robust data-driven personalized model in consideration of human activity variations. To investigate heterogeneous thermal state of occupants, wearable sensors and machine learning algorithms were used to continuously monitor and analyse individual physiological signals, activity-based metabolic rates and environmental indices. Field experiments were conducted with 10 subjects in a campus building in the US, and the results showed that predictive models considering metabolic rate yield advanced performance of up to 8.5%, implying that activity-based metabolic rates provide better understanding of personal thermal comfort. This paper quantitatively validates the effectiveness of reflecting metabolic rate based on human activity variations into personal thermal comfort modelling, which provides an insight into how to better model personal thermal comfort of occupants in real-life settings.

ACS Style

Jeehee Lee; Youngjib Ham. Physiological sensing-driven personal thermal comfort modelling in consideration of human activity variations. Building Research & Information 2020, 49, 512 -524.

AMA Style

Jeehee Lee, Youngjib Ham. Physiological sensing-driven personal thermal comfort modelling in consideration of human activity variations. Building Research & Information. 2020; 49 (5):512-524.

Chicago/Turabian Style

Jeehee Lee; Youngjib Ham. 2020. "Physiological sensing-driven personal thermal comfort modelling in consideration of human activity variations." Building Research & Information 49, no. 5: 512-524.

Journal article
Published: 07 November 2020 in Sustainable Cities and Society
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Utility poles would collapse by their structural instability as well as time-dependent material deterioration. Particularly, the moment carrying capacity of leaning poles would be dramatically reduced during extreme wind events. In this paper, we regard leaning poles as warning signs of potential failures that can affect the power distribution network performance and estimate the failure probability of leaning poles. To analyze the moment behavior of leaning poles, we propose a new probabilistic framework for computing three types of loads by wind pressure, overturning force, and conductor tension. A set of fragility curves of utility poles with given ages and leaning angles are presented to assess the impact of leaning on the probability of failure. The proposed analytics are tested through a case study on the parts of the power distribution network in Houston, TX. By examining the progress of failure in the network, this method enables to analyze potentially vulnerable utility poles that are likely to threaten the power distribution system reliability under varying wind speed. Thus, this research has the potential to support risk-informed decision-making for power distribution infrastructure systems and ultimately enhance the urban community resilience to blackouts caused by the power distribution system disruption in extreme weather.

ACS Style

Seulbi Lee; Youngjib Ham. Probabilistic framework for assessing the vulnerability of power distribution infrastructures under extreme wind conditions. Sustainable Cities and Society 2020, 65, 102587 .

AMA Style

Seulbi Lee, Youngjib Ham. Probabilistic framework for assessing the vulnerability of power distribution infrastructures under extreme wind conditions. Sustainable Cities and Society. 2020; 65 ():102587.

Chicago/Turabian Style

Seulbi Lee; Youngjib Ham. 2020. "Probabilistic framework for assessing the vulnerability of power distribution infrastructures under extreme wind conditions." Sustainable Cities and Society 65, no. : 102587.

Journal article
Published: 01 November 2020 in Journal of Management in Engineering
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The performance of a power grid after a disaster is associated with the grid’s ability to meet electricity demand under supply disruption. Prior studies primarily focused on reducing risk through reinforcement of supply-side reliability. However, during or after a disaster, electricity demand is likely to vary (e.g., electricity use may increase for disaster relief supply production or decrease because of governmental energy conservation policies). This study examined the impact of demand-side responses from industrial, commercial, and residential sectors on the performance of the power grid when supply shortages occur after earthquakes. Electricity supply and demand in the context of seismic hazards were modeled based on the system dynamics approach, and the model was tested with seismic hazards that occurred in South Korea in 2016. The simulation results showed that without supply growth after seismic hazards, the blackout decreased as much as 6.7% of daily electricity use in the case region when commercial and residential sectors give positive aid for participating in mandatory or voluntary demand control, or both. This finding demonstrates the critical role of demand-side management, which can regulate electricity consumption to improve community resilience. The outcomes of this research have the potential to support governmental policymaking to determine optimal values of generation capacity expansion, load shedding, and voluntary savings to prevent supply shortages and oversupply.

ACS Style

Seulbi Lee; Moonseo Park; Hyun-Soo Lee; Youngjib Ham. Impact of Demand-Side Response on Community Resilience: Focusing on a Power Grid after Seismic Hazards. Journal of Management in Engineering 2020, 36, 04020071 .

AMA Style

Seulbi Lee, Moonseo Park, Hyun-Soo Lee, Youngjib Ham. Impact of Demand-Side Response on Community Resilience: Focusing on a Power Grid after Seismic Hazards. Journal of Management in Engineering. 2020; 36 (6):04020071.

Chicago/Turabian Style

Seulbi Lee; Moonseo Park; Hyun-Soo Lee; Youngjib Ham. 2020. "Impact of Demand-Side Response on Community Resilience: Focusing on a Power Grid after Seismic Hazards." Journal of Management in Engineering 36, no. 6: 04020071.

Journal article
Published: 05 October 2020 in Automation in Construction
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Emerging vision-based frameworks have demonstrated the great potential to robustly perform volumetric measurements on point cloud models, which has several applications for site material management (e.g., during earthworks). However, prevalent vision-based frameworks to date involve human interventions to manually trim objects of interest from point cloud models, which would be time-consuming and labor-intensive. In addition, point cloud models for volumetric measurements are often incomplete and noisy. To address such challenges, we automatically detect and segment target objects in point cloud models via a deep learning-based approach and then map the semantic values onto point cloud models for 3D semantic segmentation. Once target objects are segmented, the associated volumes are quantified through the proposed vision-based computational process. For evaluation, case studies were performed on material piles in the real-world. The proposed method has the potential to enhance vision-based volumetric measurements, which supports systematic decision-making for material management in jobsites.

ACS Style

Mirsalar Kamari; Youngjib Ham. Vision-based volumetric measurements via deep learning-based point cloud segmentation for material management in jobsites. Automation in Construction 2020, 121, 103430 .

AMA Style

Mirsalar Kamari, Youngjib Ham. Vision-based volumetric measurements via deep learning-based point cloud segmentation for material management in jobsites. Automation in Construction. 2020; 121 ():103430.

Chicago/Turabian Style

Mirsalar Kamari; Youngjib Ham. 2020. "Vision-based volumetric measurements via deep learning-based point cloud segmentation for material management in jobsites." Automation in Construction 121, no. : 103430.

Journal article
Published: 01 May 2020 in Journal of Management in Engineering
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Defining, measuring, and dealing with contractual risks are crucial for successful construction projects because the contractual risks can lead to serious claims and disputes. In general, construction participants make a stipulation regarding their roles and responsibilities by contracting in order to prevent such claims and disputes. A common practice for preparing construction contracts is to modify the standard contract forms to reflect the interests of the given project from the owner’s perspective. In this process, however, favorable clauses that may be beneficial to the contractor are often modified or even removed, causing significant potential risks to the contractor. Therefore, an in-depth review of contract terms and conditions is required to avoid future risks. This study presents a new proactive risk assessment model to identify missing contractor-friendly clauses in the owner’s modified contract conditions from the contractor’s point of view. A case study is used to demonstrate the proposed framework, and real-world project cases were analyzed to understand what type of contractor-friendly clauses would likely be omitted in the owner’s modified contract. In this study, the developed model builds on rule-based natural-language processing (NLP) to analyze unstructured text data through preprocessing, syntactic analysis, and semantic analysis. The proposed data-driven risk assessment model is expected to reduce the extent of human errors by (1) identifying potential contractual risks that could arise disputes; and (2) supporting to develop an appropriate response strategy for the given risks.

ACS Style

Jeehee Lee; Youngjib Ham; June-Seong Yi; Jeongwook Son. Effective Risk Positioning through Automated Identification of Missing Contract Conditions from the Contractor’s Perspective Based on FIDIC Contract Cases. Journal of Management in Engineering 2020, 36, 05020003 .

AMA Style

Jeehee Lee, Youngjib Ham, June-Seong Yi, Jeongwook Son. Effective Risk Positioning through Automated Identification of Missing Contract Conditions from the Contractor’s Perspective Based on FIDIC Contract Cases. Journal of Management in Engineering. 2020; 36 (3):05020003.

Chicago/Turabian Style

Jeehee Lee; Youngjib Ham; June-Seong Yi; Jeongwook Son. 2020. "Effective Risk Positioning through Automated Identification of Missing Contract Conditions from the Contractor’s Perspective Based on FIDIC Contract Cases." Journal of Management in Engineering 36, no. 3: 05020003.

Technical papers
Published: 01 May 2020 in Journal of Management in Engineering
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The benefits of a digital twin city have been assessed based on real-time data collected from preinstalled Internet of Things (IoT) sensors (e.g., traffic, energy use, air pollution, water quality) for managing the complex systems of cities, but the sensor-based reality information is likely insufficient to provide dynamic spatiotemporal information about physical vulnerabilities. Understanding cities’ current states of physical vulnerability can support city decision makers in analyzing associated potential risk in urban areas for data-driven infrastructure management in extreme weather events. As a step toward creating a digital twin city for effective risk-informed decision-making, this paper proposes a new framework to bring crowdsourced visual data-based reality information into a three-dimensional (3D) virtual city for a model update with interactive and immersive visualization. Unstructured visual data are collected from participatory sensing and analyzed to estimate the geospatial information of vulnerable objects in the distance representing physical vulnerability in cities. The crowdsourced visual data–based reality information of physical vulnerability in a given region is then integrated with a 3D virtual city model, and the updated 3D city model is fed into a computer-aided virtual environment (CAVE) for immersive visualization to enable users to navigate the intersection of reality and virtuality. To test the proposed framework, case studies were conducted on Houston. The outcomes demonstrate that the proposed method has the potential to make the virtual city model live in terms of local vulnerability. The digital twin city building on crowdsourced visual data is expected to contribute to risk-informed decision-making for infrastructure management in cities and help analyze various what-if scenarios in disaster situations with increased visibility of hazard and city interactions.

ACS Style

Youngjib Ham; Jaeyoon Kim. Participatory Sensing and Digital Twin City: Updating Virtual City Models for Enhanced Risk-Informed Decision-Making. Journal of Management in Engineering 2020, 36, 04020005 .

AMA Style

Youngjib Ham, Jaeyoon Kim. Participatory Sensing and Digital Twin City: Updating Virtual City Models for Enhanced Risk-Informed Decision-Making. Journal of Management in Engineering. 2020; 36 (3):04020005.

Chicago/Turabian Style

Youngjib Ham; Jaeyoon Kim. 2020. "Participatory Sensing and Digital Twin City: Updating Virtual City Models for Enhanced Risk-Informed Decision-Making." Journal of Management in Engineering 36, no. 3: 04020005.

Journal article
Published: 13 September 2019 in Automation in Construction
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Although the benefit of participatory sensing for collecting local data over large areas has long been recognized, it has not been widely used for various applications such as disaster preparation due to a lack of geospatial localization capability with respect to a distant object. In such applications, objects of interest need to be robustly localized and documented for supporting data-driven decision-making in site inspection and resource mobilization. However, participatory sensing is inappropriate to localize a distant object due to the absence of ranging sensors in citizens' mobile devices; thus, the localization accuracy varies to a large extent. To address this issue, this study presents a novel geospatial localization method for distant objects based on participatory sensing. The proposed geospatial localization process consists of multiple computational modules—a geographic coordinate conversion, mean-shift clustering, deep learning-based object detection, magnetic declination adjustment, line of sight equation formulation, and the Moore-Penrose generalized inverse method—to improve the localization accuracy in participatory sensing environments. The experiments were conducted in Houston and College Station in Texas to evaluate the proposed method, and the experimental results demonstrated a reasonable localization accuracy, recording the distance errors of 1.5 m to 27.8 m when the distance from observers to the objects of interest were 17 m to 296 m. The proposed method is expected to contribute to rapid data collection over large urban areas, thereby facilitating disaster preparedness that needs to identify locations of distant objects at risk.

ACS Style

Hongjo Kim; Youngjib Ham. Participatory sensing-based geospatial localization of distant objects for disaster preparedness in urban built environments. Automation in Construction 2019, 107, 102960 .

AMA Style

Hongjo Kim, Youngjib Ham. Participatory sensing-based geospatial localization of distant objects for disaster preparedness in urban built environments. Automation in Construction. 2019; 107 ():102960.

Chicago/Turabian Style

Hongjo Kim; Youngjib Ham. 2019. "Participatory sensing-based geospatial localization of distant objects for disaster preparedness in urban built environments." Automation in Construction 107, no. : 102960.

Journal article
Published: 24 July 2019 in Sensors
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Due to the nature of real-world problems in civil engineering, students have had limited hands-on experiences in structural dynamics classes. To address this challenge, this paper aims to bring real-world problems in structural dynamics into classrooms through a new interactive learning tool that promotes physical interaction among students and enhances their engagement in classrooms. The main contribution is to develop and test a new interactive computing system that simulates structural dynamics by integrating a dynamic model of a structure with multimodal sensory data obtained from mobile devices. This framework involves integrating multiple physical components, estimating students’ motions, applying these motions as inputs to a structural model for structural dynamics, and providing students with an interactive response to observe how a given structure behaves. The mobile devices will capture dynamic movements of the students in real-time and take them as inputs to the dynamic model of the structure, which will virtually simulate structural dynamics affected by moving players. Each component of synchronizing the dynamic analysis with motion sensing is tested through case studies. The experimental results promise the potential to enable complex theoretical knowledge in structural dynamics to be more approachable, leading to more in-depth learning and memorable educational experiences in classrooms.

ACS Style

HyungChul Yoon; Kevin Han; Youngjib Ham. A Framework of Human-Motion Based Structural Dynamics Simulation Using Mobile Devices. Sensors 2019, 19, 3258 .

AMA Style

HyungChul Yoon, Kevin Han, Youngjib Ham. A Framework of Human-Motion Based Structural Dynamics Simulation Using Mobile Devices. Sensors. 2019; 19 (15):3258.

Chicago/Turabian Style

HyungChul Yoon; Kevin Han; Youngjib Ham. 2019. "A Framework of Human-Motion Based Structural Dynamics Simulation Using Mobile Devices." Sensors 19, no. 15: 3258.

Journal article
Published: 09 May 2019 in Automation in Construction
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In recent years, emerging mobile devices and camera-equipped platforms have offered a great convenience to visually capture and constantly document the as-is status of construction sites. In this regard, visual data are regularly collected in the form of numerous photos or lengthy videos. However, massive amounts of visual data that are being collected from jobsites (e.g., data collection on daily or weekly bases by Unmanned Aerial Vehicles, UAVs) has provoked visual data overload as an inevitable problem to face. To address such data overload issue in the construction domain, this paper aims at proposing a new method to automatically retrieve photo-worthy frames containing construction-related contents that are scattered in collected video footages or consecutive images. In the proposed method, the presence of objects of interest (i.e., construction-related contents) in given image frames are recognized by the semantic segmentation, and then scores of the image frames are computed based on the spatial composition of the identified objects. To improve the filtering performance, high-score image frames are further analyzed to estimate their likelihood to be intentionally taken. Case studies in two construction sites have revealed that the accuracy of the proposed method is close-to-human judgment in filtering visual data to retrieve photo-worthy image frames containing construction-related contents. The performance metrics demonstrate around 91% of accuracy in the semantic segmentation, and we observed enhanced human-like judgment in filtering construction visual data comparing to prior works. It is expected that the proposed automated method enables practitioners to assess the as-is status of construction sites efficiently through selective visual data, thereby facilitating data-driven decision making at the right time.

ACS Style

Youngjib Ham; Mirsalar Kamari. Automated content-based filtering for enhanced vision-based documentation in construction toward exploiting big visual data from drones. Automation in Construction 2019, 105, 102831 .

AMA Style

Youngjib Ham, Mirsalar Kamari. Automated content-based filtering for enhanced vision-based documentation in construction toward exploiting big visual data from drones. Automation in Construction. 2019; 105 ():102831.

Chicago/Turabian Style

Youngjib Ham; Mirsalar Kamari. 2019. "Automated content-based filtering for enhanced vision-based documentation in construction toward exploiting big visual data from drones." Automation in Construction 105, no. : 102831.

Journal article
Published: 01 March 2019 in Automation in Construction
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Although video surveillance systems have shown potential for analyzing jobsite contexts, the necessity of a complex multi-camera surveillance system or workers' privacy issues remain as substantive hurdles to adopt such systems in practice. To address such issues, this study presents a non-intrusive earthmoving productivity analysis method using imaging and simulation. The site access log of dump trucks is used to infer earthmoving contexts, which is produced by analyzing videos recorded at the entrance and the exit of a construction site. An algorithm for license plate detection and recognition in an uncontrolled environment is developed to automatically produce the site access log, by leveraging video deinterlacing, a deep convolutional network, and rule-based post-processing. The experimental results show the effectiveness of the proposed method for producing the site access log. Based on the site access log, simulation-based productivity analysis is conducted to produce a daily productivity report, which can provide the basis for earthmoving resource planning. It is expected that the resulting daily productivity report promotes data-driven decision-making for earthmoving resource allocation, thereby improving potential for saving cost and time for earthworks with an updated resource allocation plan.

ACS Style

Hongjo Kim; Youngjib Ham; Wontae Kim; Somin Park; Hyoungkwan Kim. Vision-based nonintrusive context documentation for earthmoving productivity simulation. Automation in Construction 2019, 102, 135 -147.

AMA Style

Hongjo Kim, Youngjib Ham, Wontae Kim, Somin Park, Hyoungkwan Kim. Vision-based nonintrusive context documentation for earthmoving productivity simulation. Automation in Construction. 2019; 102 ():135-147.

Chicago/Turabian Style

Hongjo Kim; Youngjib Ham; Wontae Kim; Somin Park; Hyoungkwan Kim. 2019. "Vision-based nonintrusive context documentation for earthmoving productivity simulation." Automation in Construction 102, no. : 135-147.

Journal article
Published: 01 February 2019 in Journal of Construction Engineering and Management
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Poor data quality in pavement construction life-cycle inventory (LCI) causes uncertainty in quantifying the associated environmental impact through life-cycle assessment (LCA). To reduce such LCA uncertainty while enhancing the reliability, several studies have been conducted on a screening procedure based on a quality assessment of the LCI input data to identify main sources of the resulting uncertainty. However, they often create additional uncertainty in the screening process and thus result in erroneous outcomes in identifying main uncertainty sources. This paper proposes a new system-level approach that enables the identification of main uncertainty sources through input data quality assessment upon reducing additional uncertainty. Based on the proposed preset criteria and by leveraging environmental emission quantities associated with each process, the authors first propose to achieve a consistent weighting process and then derive the system-level aggregated data quality indicator (ADQI). By utilizing the ADQI, system-level LCA uncertainty information is obtained through a modified beta distribution. The proposed method was evaluated through case studies on real-world pavement construction projects of the Illinois Tollway, and the main uncertainty sources, named key processes, were identified through sensitivity analyses. In the case studies, the plant operation, cement production, and binder production were identified as key processes in the given pavement construction project, contributing more than half of the total uncertainty resulting from poor data quality. Based on these findings, the proposed work is expected to help practitioners improve the reliability of pavement construction LCA through uncertainty-informed decision making to better reflect real project characteristics in the identified key processes.

ACS Style

Wonjae Yoo; Hasan Ozer; Youngjib Ham. System-Level Approach for Identifying Main Uncertainty Sources in Pavement Construction Life-Cycle Assessment for Quantifying Environmental Impacts. Journal of Construction Engineering and Management 2019, 145, 04018137 .

AMA Style

Wonjae Yoo, Hasan Ozer, Youngjib Ham. System-Level Approach for Identifying Main Uncertainty Sources in Pavement Construction Life-Cycle Assessment for Quantifying Environmental Impacts. Journal of Construction Engineering and Management. 2019; 145 (2):04018137.

Chicago/Turabian Style

Wonjae Yoo; Hasan Ozer; Youngjib Ham. 2019. "System-Level Approach for Identifying Main Uncertainty Sources in Pavement Construction Life-Cycle Assessment for Quantifying Environmental Impacts." Journal of Construction Engineering and Management 145, no. 2: 04018137.

Journal article
Published: 01 August 2018 in Automation in Construction
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This study presents an integrated method of construction-process simulation and vision-based context reasoning for productivity analysis of an earthmoving process in a tunnel. Convolutional networks are used to detect construction equipment in the tunnel CCTV video and the context of the earthmoving process is inferred by the context reasoning process. The construction equipment detection model exhibited enhanced performance, with a mean average precision of 99.09%, and the error rate of the estimated context information was only 1.6% of the actual earthmoving context measured by a human. The estimated context information was used as an input for the WebCYCLONE simulation to generate a productivity and cost analysis report. Sensitivity analysis regarding construction equipment provided a new equipment allocation plan that could reduce the cost of the current earthmoving process by 12.25%.

ACS Style

Hongjo Kim; Seongdeok Bang; Hoyoung Jeong; Youngjib Ham; Hyoungkwan Kim. Analyzing context and productivity of tunnel earthmoving processes using imaging and simulation. Automation in Construction 2018, 92, 188 -198.

AMA Style

Hongjo Kim, Seongdeok Bang, Hoyoung Jeong, Youngjib Ham, Hyoungkwan Kim. Analyzing context and productivity of tunnel earthmoving processes using imaging and simulation. Automation in Construction. 2018; 92 ():188-198.

Chicago/Turabian Style

Hongjo Kim; Seongdeok Bang; Hoyoung Jeong; Youngjib Ham; Hyoungkwan Kim. 2018. "Analyzing context and productivity of tunnel earthmoving processes using imaging and simulation." Automation in Construction 92, no. : 188-198.

Conference paper
Published: 22 July 2018 in Proceedings of the 35th International Symposium on Automation and Robotics in Construction (ISARC)
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ACS Style

Hongjo Kim; Seongdeok Bang; Hoyoung Jeong; Youngjib Ham; Hyoungkwan Kim. Integration of Imaging and Simulation for Earthmoving Productivity Analysis. Proceedings of the 35th International Symposium on Automation and Robotics in Construction (ISARC) 2018, 711 -714.

AMA Style

Hongjo Kim, Seongdeok Bang, Hoyoung Jeong, Youngjib Ham, Hyoungkwan Kim. Integration of Imaging and Simulation for Earthmoving Productivity Analysis. Proceedings of the 35th International Symposium on Automation and Robotics in Construction (ISARC). 2018; ():711-714.

Chicago/Turabian Style

Hongjo Kim; Seongdeok Bang; Hoyoung Jeong; Youngjib Ham; Hyoungkwan Kim. 2018. "Integration of Imaging and Simulation for Earthmoving Productivity Analysis." Proceedings of the 35th International Symposium on Automation and Robotics in Construction (ISARC) , no. : 711-714.

Research paper
Published: 25 April 2018 in Building Research & Information
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The gap between the architectural information and the as-is building condition has been known as one of the pivotal factors influencing deviations between actual and predicted building energy consumption. Despite such significance, quantifying the impact of deviated building information on energy use has not been fully investigated. This paper explores building information modelling (BIM)-driven experimental simulation to quantify the impact of building envelope condition on energy use, which can infer the impact of reflecting the as-is building conditions in as-designed BIMs on the reliability of energy analysis. First, BIM-driven energy simulations are conducted with varied thermo-physical properties of building envelope elements in gbXML-based BIMs under different climate conditions. Building upon the impacting factor for energy analysis (IFEA), the simulation results are then used to infer the impact of the deviated building condition on energy consumption. Through case studies, it is observed that the annual energy consumption of a residential building can deviate by 18–20%, whereas thermal resistances of exterior walls can deviate by 1 m2K/W. This paper validates quantitatively the potential benefits of reflecting the as-is building condition in BIM-based energy performance analysis. This provides practitioners with insights into how to improve the reliability of energy analysis of existing buildings.

ACS Style

Jongwook Jeon; Jaehyuk Lee; Youngjib Ham. Quantifying the impact of building envelope condition on energy use. Building Research & Information 2018, 47, 404 -420.

AMA Style

Jongwook Jeon, Jaehyuk Lee, Youngjib Ham. Quantifying the impact of building envelope condition on energy use. Building Research & Information. 2018; 47 (4):404-420.

Chicago/Turabian Style

Jongwook Jeon; Jaehyuk Lee; Youngjib Ham. 2018. "Quantifying the impact of building envelope condition on energy use." Building Research & Information 47, no. 4: 404-420.

Conference paper
Published: 29 March 2018 in Construction Research Congress 2018
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Nowadays, to assess and document construction and building performance, large amount of visual data are captured and stored through camera equipped platforms such as wearable cameras, unmanned aerial/ground vehicles, and smart phones. However, due to the nonstop fashion in recording such visual data, not all of the frames in captured consecutive footages are intentionally taken, and thus not every frame is worthy of being processed for construction and building performance analysis. Since many frames will simply have non-construction related contents, before processing the visual data, the content of each recorded frame should be manually investigated depending on the association with the goal of the visual assessment. To address such challenges, this paper aims to automatically filter construction big visual data that requires no human annotations. To overcome challenges in pure discriminative approach using manually labeled images, we construct a generative model with unlabeled visual dataset, and use it to find construction-related frames in big visual dataset from jobsites. First, through composition-based snap point detection together with domain adaptation, we filter and remove most of accidently recorded frames in the footage. Then, we create discriminative classifier trained with visual data from jobsites to eliminate non-construction related images. To evaluate the reliability of the proposed method, we have obtained the ground truth based on human judgment for each photo in our testing dataset. Despite learning without any explicit labels, the proposed method shows a reasonable practical range of accuracy, which generally outperforms prior snap point detection. Through the case studies, the fidelity of the algorithm is discussed in detail. By being able to focus on selective visual data, practitioners will spend less time on browsing large amounts of visual data; rather spend more time on looking at how to leverage the visual data to facilitate decision-makings in built environments.

ACS Style

Mirsalar Kamari; Youngjib Ham. Automated Filtering Big Visual Data from Drones for Enhanced Visual Analytics in Construction. Construction Research Congress 2018 2018, 1 .

AMA Style

Mirsalar Kamari, Youngjib Ham. Automated Filtering Big Visual Data from Drones for Enhanced Visual Analytics in Construction. Construction Research Congress 2018. 2018; ():1.

Chicago/Turabian Style

Mirsalar Kamari; Youngjib Ham. 2018. "Automated Filtering Big Visual Data from Drones for Enhanced Visual Analytics in Construction." Construction Research Congress 2018 , no. : 1.