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共 2005 条记录，以下是131-140 订阅

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Dependency Parsing-Based Information Extraction from Car Crash Narratives to Support Crash Scene Reconstruction

Dependency Parsing-Based Information Extraction from Car Cra...

引用

asce international conference on computing in civil engineering (I3CE)

作者： Li, Hang Zhang, Jiansong Chen, Yunfeng Feng, Yiheng Proctor, Robert W. Purdue Univ Sch Construct Management Technol W Lafayette IN 47907 USA Purdue Univ Lyles Sch Civil Engn W Lafayette IN 47907 USA Purdue Univ Dept Psychol Sci W Lafayette IN 47907 USA

ISBN: (数字)9780784485224

ISBN: (纸本)9780784485224

Crash scene reconstruction is essential for reverse-engineering the factors of a crash scene to determine the cause of a crash. It requires automated information extraction (IE) from the textual crash report narratives and their formalization in a computer-processable representation. Natural language processing (NLP) is a powerful computational tool to process texts. This paper presents a dependency parsing (DP)-based NLP system for automated IE of crash events information from crash report narratives. DP-based rules and patterns were leveraged for defining relations between subjects and objects to support the IE algorithm. The proposed IE system was tested on 50 reports collected from the Southeast Michigan Council of Governments (SEMCOG) traffic crash database, which achieved an overall 94.9% precision, 90.2% recall, and 92.5% F1-score. A parallel experiment was conducted with ChatGPT to extract information from crash narratives, where an 88.0% precision and 88.0% recall were obtained.

关键词： Natural language processing systems

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Understanding the Impact of Sensing Flexibility and Strategies on HVAC Energy Consumption Modeling

Understanding the Impact of Sensing Flexibility and Strategi...

引用

asce international conference on computing in civil engineering (I3CE)

作者： Bucarelli, Nidia El-Gohary, Nora Univ Illinois Dept Civil & Environm Engn Urbana IL 61801 USA

ISBN: (数字)9780784485248

ISBN: (纸本)9780784485248

Data-driven HVAC energy consumption modeling could play an important role in operating energy-efficient buildings. However, (1) existing data-driven approaches rely on rigid sensor networks to collect indoor physical parameter data, often deployed in an ad-hoc manner or based on engineering judgment;and (2) the impact of this practice on model performance is often poorly understood. To address this gap, this paper aims to assess the impact of sensor deployment configurations on HVAC energy consumption modeling, where a configuration is defined in terms of the number and locations of sensors and their flexibility (i.e., fixed or can change over time). Indoor temperature and humidity data were collected from an office building. Several configurations were defined and evaluated in predicting the HVAC consumption, using an XGBoost-based model. The results showed that sensor configurations could significantly impact the performance of HVAC consumption modeling, and that periodic changes in sensor locations could improve the performance of traditional rigid methods. The findings from this work could help define improved sensor configurations for enhanced HVAC management and operation.

关键词： Energy utilization

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A Review of Non-Invasive Heat Stress Monitoring Systems for Construction Workers Using Wearable Device and Computer Vision Technology

A Review of Non-Invasive Heat Stress Monitoring Systems for ...

引用

asce international conference on computing in civil engineering (I3CE)

作者： Khorshid, Sepehr Song, Siyuan Chen, Kaiwen Lyu, Peiyi Univ Alabama Dept Civil Construct & Environm Engn Safety Automat & Visualizat Environm Lab Tuscaloosa AL 35487 USA Univ Alabama Dept Civil Construct & Environm Engn Tuscaloosa AL 35487 USA

ISBN: (数字)9780784485248

ISBN: (纸本)9780784485248

Severe heat waves pose significant health risks to individuals. Under extreme heat conditions, construction workers are susceptible to dual threats from heat stress, which not only reduce their productivity but also have severe implications for their health. Simply raising awareness or monitoring environmental conditions may not suffice to protect individuals from the dangers of heat stress. Given the significance of individual variations in heat tolerance, this research aims to explore heat stress physiological smart monitoring systems, with a particular focus on wearable devices and computer vision (CV) technologies. A systematic literature review was conducted. After evaluating monitoring technologies and their respective strengths and weaknesses, this study proposes the potential for a proactive approach to monitoring thermal stress in construction workers. This approach, which involves using both wearable monitoring devices and CV technology, is suggested as a viable solution to proactively prevent heat stress incidents that could jeopardize human life.

关键词： Health risks

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A Framework for Assessing the Building Spatial Design from Fire Evacuation Perspective

A Framework for Assessing the Building Spatial Design from F...

引用

asce international conference on computing in civil engineering (I3CE)

作者： Fu, Rong Kan, Ruizhe Seo, Hyungjoon Yue, Yong Zhang, Cheng Xian Jiaotong Liverpool Univ Dept Civil Engn Suzhou Peoples R China Xian Jiaotong Liverpool Univ Dept Comp Suzhou Peoples R China Univ Liverpool Dept Civil Engn & Ind Design Liverpool England

ISBN: (数字)9780784485248

ISBN: (纸本)9780784485248

Spatial design greatly influences fire evacuation in buildings. Although the existing research has comprehensively assessed building spatial design in terms of building circulation and overlooked evaluating building evacuation circulation and how building spatial design impacts occupants' cognitive processes during a fire emergency. Therefore, this paper proposes a framework to investigate the effectiveness of building evacuation impacted by building spatial design by integrating building information model (BIM), fire simulation, and virtual reality (VR) technology. A case study is carried out to evaluate building spatial design in terms of building circulation and emergency signage. Preliminary results demonstrated that most participants preferred to select the path with the shortest safety egress distance, fewer turns, and associated with less accessible spaces. Moreover, insufficient installation of signs will lead a few participants to select the longer evacuation path and waste much effort in searching for helpful information on evacuation-irrelevant objects, which reduces fire evacuation efficiency.

关键词： Fires

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Deep Learning Method to Detect and Locate Signages from 2D Drawings for Semantic Enrichment of BIM

Deep Learning Method to Detect and Locate Signages from 2D D...

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asce international conference on computing in civil engineering (I3CE)

作者： Duan, Tianle Nguyen, Thi Qui Yeoh, Justin K. W. Natl Univ Singapore Dept Civil & Environm Engn Coll Design & Engn Singapore Singapore Singapore Inst Technol Civil Engn Program Engn Cluster Singapore Singapore

ISBN: (数字)9780784485231

ISBN: (纸本)9780784485231

A reasonable signage layout can reduce evacuees' operational delays during emergencies. Building information modeling (BIM) with detailed signage information is essential for automatic checking and improving the signage layout. However, in current practice, exit signs of buildings are presented mostly in 2D drawing formats rather than in BIM. Manually adding these signages into BIM is time-consuming and labor-intensive, and reconstructing them with on-site data requires considerable device costs and workload. Hence, there is a need for a convenient, low-cost, and widely applicable approach to extract signage information from 2D drawings and add them into BIM. Related works on 2D drawings mainly focused on architectural and structural components, but those methods cannot directly apply to exit signs. This study proposes a deep learning method combined with pre-set rules to detect and locate exit signs from 2D drawings. The extracted information is expected to enrich existing building models.

关键词： Information theory

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Request for Information (RFI) Recommender System for Pre-Construction Design Review Application Using Natural Language Processing, Chat-GPT, and Computer Vision

Request for Information (RFI) Recommender System for Pre-Con...

引用

asce international conference on computing in civil engineering (I3CE)

作者： Panahi, Roshan Kivlin, John-Paul Louis, Joseph Oregon State Univ Sch Civil & Construct Engn Corvallis OR 97331 USA Skanska Portland OR USA Oregon State Univ Dept Civil & Construct Engn Corvallis OR 97331 USA

ISBN: (数字)9780784485224

ISBN: (纸本)9780784485224

Design reviews are critical for construction projects to reduce costly reworks and future conflicts. However, this is a challenging task due to uncertainties during the initial stages of a project, which can lead to numerous requests for information (RFIs). With the recent advancements in language models and computer vision, a large volume of historical RFIs can be leveraged to aid design reviews. This study proposes a novel framework using natural language processing, ChatGPT API, and computer vision techniques to identify the RFIs from previous projects that are more likely to reoccur in the project under review. The framework was tested using RFI data from 19 healthcare construction projects, and a web application was used to evaluate user experiences with the tool. Successful implementation of the proposed framework could reduce the number of RFIs, change orders, rework by contractors, and the likelihood of time and cost overruns for construction projects.

关键词： design review request for information (RFI) natural language processing computer vision Chat-GPT

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Automatic Extraction of Curbs and Curb Ramps from Mobile Lidar Point Clouds

Automatic Extraction of Curbs and Curb Ramps from Mobile Lid...

引用

asce international conference on computing in civil engineering (I3CE)

作者： Che, Erzhuo Olsen, Michael J. Turkan, Yelda Oregon State Univ Sch Civil & Construct Engn Corvallis OR 97331 USA

ISBN: (数字)9780784485224

ISBN: (纸本)9780784485224

Mobile lidar is a remote sensing technique adopted by several transportation agencies to collect dense 3D point clouds with high accuracy and efficiency on a regular basis for a wide variety of applications (e.g., asset management, civil design, etc.). Leveraging mobile lidar technology can substantially improve the conventional procedure of asset management. However, there is still a need for automatic tools to process these 3D point clouds effectively and efficiently. In this study, we developed an automatic workflow to extract curbs and localize curb ramps in large point cloud datasets. The proposed method consists of three steps: Vo-SmoG ground filtering followed by refinement for preserving curbs, curb detection based on the sudden elevation change and linearity of the curbs, and curb ramp localization leveraging the context provided by the curb lines. It was evaluated both qualitatively and quantitatively with a representative mobile lidar dataset, resulting in recall, precision, and F-1 scores of 72.4%. Beyond extracting curbs and curb ramps, the proposed approach can be potentially used for further analyses such as feature characterization and point cloud classification for other assets and objects of interest.

关键词： Asset management

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Assessing the Impact of Construction Work Zone Layout on Traffic Safety: A Driving Simulator Experiment

Assessing the Impact of Construction Work Zone Layout on Tra...

引用

asce international conference on computing in civil engineering (I3CE)

作者： Liu, Peng Zhang, Chengyi Zhu, Qin Li, Huimin Jacobs, Francois Univ Wyoming Dept Civil & Architectural Engn & Construct Manag Construct Res & Innovat Lab Laramie WY 82071 USA Univ Wyoming Dept Civil & Architectural Engn & Construct Manag Laramie WY 82071 USA Univ Wyoming Dept Div Kinesiol & Hlth Laramie WY 82071 USA North China Univ Water Resources & Elect Power Dept Construct Engn & Management Zhengzhou Peoples R China

ISBN: (数字)9780784485224

ISBN: (纸本)9780784485224

Work zones for road construction, maintenance, and enhancement activities affect traffic safety and reduce road network efficiency. This study aims to investigate the effect of work zone layout on traffic safety by designing and conducting a driving simulator experiment. Twenty participants were recruited to drive through three different scenarios, including a scenario without heavy equipment, a scenario with heavy equipment near the closed lane at the beginning of the work zone (position 1), and a scenario with heavy equipment away from the closed lane at the end of the work zone (position 2). The driving behaviors of the participants, including longitudinal velocity and lateral offset, were monitored and collected. The results suggest that heavy equipment in construction work zone should be placed at position 2 from a safety perspective.

关键词： Roads and streets

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A Digital Twin Framework for Bridges

A Digital Twin Framework for Bridges

引用

asce international conference on computing in civil engineering (I3CE)

作者： Najafi, Amirali Amir, Zaid Salman, Baris Sanaei, Parisa Lojano-Quispe, Erick Maher, Ali Schaefer, Richard Rutgers State Univ Ctr Adv Infrastruct & Transportat Piscataway NJ 08854 USA Rutgers State Univ Dept Civil & Environm Engn Piscataway NJ USA Syracuse Univ Dept Civil & Environm Engn Syracuse NY 13244 USA New Jersey Transit Newark NJ USA

ISBN: (数字)9780784485231

ISBN: (纸本)9780784485231

Current inspection and maintenance practices for bridges involve periodic manual visual surveys which are time consuming and cumbersome due to number of bridges in need of assessment. Unprecedented capabilities have become possible in recent years thanks to strides made in sensing technologies, computer vision, and machine learning. These technologies can be integrated into a digital twin framework for better insight into a bridge's structural integrity, maintenance needs, and potential risks. A digital twin framework involves creating a virtual model that mimics the behavior and performance of a physical bridge. Data collection about a bridge's physical condition is the first step in developing a framework. Physics simulation and graphical environment are next needed to host relevant models of the bridge. An illustrative example involving these steps and digital twin use for scenario simulation for different mechanisms is provided. The example focuses on modeling of a railway bridge in Lyndhurst, New Jersey.

关键词： digital twin geometry extraction bridges health monitoring scenario simulation

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Leveraging Artificial Intelligence for Enabling Personalized Activity-Based Workplaces

Leveraging Artificial Intelligence for Enabling Personalized...

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asce international conference on computing in civil engineering (I3CE)

作者： Deng, Min Wang, Xi Li, Da Fu, Bo Menassa, Carol C. Kamat, Vineet R. Univ Michigan Dept Civil & Environm Engn Ann Arbor MI 48109 USA Texas A&M Univ Dept Construct Sci College Stn TX 77843 USA Clemson Univ Dept Civil Engn Clemson SC 29631 USA Univ Michigan Dept Robot Ann Arbor MI 48109 USA

ISBN: (数字)9780784485224

ISBN: (纸本)9780784485224

Thanks to emerging technologies and increased computational power, a new wave of smart buildings is gaining attraction with a specific focus on the personal experience of individual occupants. Real-time monitoring and estimation of human states are key to achieving individual satisfaction. This paper proposes a framework to predict and track the real-time state of each individual and assist with the decision-making of room assignment and environment control. The core idea of the framework is to distinguish individuals by an idea of the personal database named human digital ID (DID). Two case studies are given to demonstrate the adoption of the framework. They show how different indoor well-being such as thermal comfort and work engagement can be incorporated into the framework. The results show improvement in the occupants' well-being after adopting the framework. The framework is scalable in every aspect and can serve as a fundamental concept for future smart buildings.

关键词： Decision making

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