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SSRN 2022年

作者： Wu, Xinyue Chow, Andy H.F. Zhuang, Li Ma, Wei Lam, William Wong, S.C. Department of Advanced Design and Systems Engineering City University of Hong Kong Kowloon Tong Hong Kong School of Cyber Science and Engineering Southeast University Nanjing China Department of Civil and Environmental Engineering The Hong Kong Polytechnic University Hung Hom Hong Kong Department of Civil Engineering The University of Hong Kong Pokfulam Hong Kong

This paper presents a Bayesian data fusion framework for estimating journey time distributions that uses a mixture distribution model to classify feeding data into different traffic states. Different from most studies, the proposed framework uses general statistical models to estimate the vehicular journey time variability. Feeding data collected from multiple data sources are classified based on the associated traffic conditions, and the corresponding estimation biases of the individual data sources are determined using a Gaussian distribution model. The proposed framework is implemented and tested on a real Hong Kong highway corridor with actual data collected from the field. The findings of the case study show significant improvement in the journey time estimations of the proposed method compared with the individual measurements. The results also highlight the benefit of incorporating a traffic state classifier in the fusion framework. This study contributes to the development of reliability-based intelligent transportation systems based on advanced traffic data analytics. © 2022, The Authors. All rights reserved.

关键词： Mixtures

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In-Stream Impact of a Real-Time Controlled Dry Detention Basin: A Case Study

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SSRN 2024年

作者： Akin, Aaron A. Rexhausen, Victoria Khojandi, Anahita Hathaway, Jon M. Seamon Whiteside & Associates Inc. 501 Wando Park Blvd #200 Mt PleasantSC29464 United States Department of Industrial and Systems Engineering University of Tennessee Knoxville 851 Neyland Drive 521 John D. Tickle Engineering Building KnoxvilleTN37996 United States Department of Civil and Environmental Engineering University of Tennessee Knoxville 851 Neyland Drive 325 John D. Tickle Engineering Building KnoxvilleTN37996 United States

Retrofitting static stormwater infrastructure with controllable valves has been investigated as a means to mitigate the effects of urbanization and overburdened infrastructure. However, few case studies exist which quantify the impact of water release from actively controlled stormwater assets on receiving waters. To address this, a dry extended detention basin was retrofitted with a controllable valve, and water quality and flow were monitored in the receiving stream. A SARIMA model was used to forecast future in-stream conditions (as if the basin’s valve was not open) using the previous 48-hours of observed data. These predicted values were compared to observed values during which the controls were operational. The basin discharge caused median increases in the stream for stage, discharge, temperature, and turbidity, while no change was observed for dissolved oxygen. As an example, the median and maximum temperature increase was 0.6oC and 4.8oC, which is a concern, but logical considering actively controlled systems often detain water for extended periods exposing the captured runoff to direct sunlight. Additional research is recommended to understand the implications of site scale, real-time controlled stormwater infrastructure on receiving waters and understand how control algorithms might be enhanced to allay these concerns. © 2024, The Authors. All rights reserved.

关键词： Real time control

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In-Stream Impact of a Real-Time Controlled Dry Detention Basin: A Case Study

SSRN

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SSRN 2024年

作者： Akin, Aaron A. Rexhausen, Victoria Khojandi, Anahita Hathaway, Jon M. Seamon Whiteside & Associates Inc. 501 Wando Park Blvd #200 Mt PleasantSC29464 United States Department of Civil and Environmental Engineering University of Tennessee Knoxville 851 Neyland Drive 325 John D. Tickle Engineering Building KnoxvilleTN37996 United States Department of Industrial and Systems Engineering University of Tennessee Knoxville 851 Neyland Drive 521 John D. Tickle Engineering Building KnoxvilleTN37996 United States

关键词： Real time control

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Utilization of Operation Research Techniques and Innovative Testing Methods for Sustainable Construction Materials

Utilization of Operation Research Techniques and Innovative ...

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IEEE International Conference on Emerging & Sustainable Technologies for Power & ICT in a Developing Society (NIGERCON)

作者： Oluwadare Joshua Oyebode Sunday Apeh Samuel-Soma M. Ajibade Chukwudulue Jedidiah Anomnakachukwu Imade Princess Okokpujie Akinniyi Tunde Akinyemi Adedotun Adetunla Olusola Anthony Ibitoye Stephen Gekwu Udeagbara Civil and Environmental Engineering Department Afe Babalola University Ado-Ekiti Ekiti State Nigeria Department of Computing and Information Systems School of Engineering and Technology Sunway University Malaysia Department of Mechanical and Mechatronics Engineering Afe Babalola University Ado-Ekiti Ekiti State Nigeria Department of Architecture Afe Babalola University Ado-Ekiti Ekiti-State Nigeria Department of Mechanical Engineering Sciences University of Johannesburg South Africa Department of Finance Afe Babalola University Ado-Ekiti Ekiti State Nigeria

ISBN: (数字)9798331542559

ISBN: (纸本)9798331542566

Innovative technology and local content cannot be achieved without appropriate techniques and testing methods to realize sustainable civil infrastructure in a challenging economy. This paper examines the utilization of operation research techniques and innovative testing methods for sustainable construction materials. Pertinent literature and empirical studies were combined to explore the theoretical foundations and real-world applications of operation research methodologies and novel testing methods for sustainable construction. Findings revealed that operation research techniques play a significant role in maximizing the consumption of sustainable construction materials. In the construction sector, ultrasonic testing (UT) is a popular non-destructive testing (NDT) method for evaluating the characteristics and structural integrity of materials like composites, steel, and concrete. Radiographic testing (RT), Computed tomography (CT), digital image correlation (DIC), and laser scanning are Ekiti State, Nigeria beneficial. A non-invasive geophysical technique called ground-penetrating radar (GPR) is used to find and map subsurface features, utilities, and irregularities in soil and building materials. Despite the advancements in operation research techniques and innovative testing methods for sustainable construction materials, several challenges persist, and future directions warrant attention to further enhance their effectiveness and applicability in construction projects. In conclusion, integrating operation research techniques and innovative testing methods represents a transformative opportunity to build a more sustainable future for the construction industry and society. Supportive policies and regulations can drive the adoption of sustainable practices and innovative technologies in the construction industry.

关键词： Operations research Correlation Digital images Computed tomography Maintenance Sustainable development Optimization civil engineering Testing Construction industry

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Mixed-Integer Programming with Enterprise Risk Analysis for Vehicle Electrification at Maritime Container Ports

Mixed-Integer Programming with Enterprise Risk Analysis for ...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Robert M. Baker Thomas L. Polmateer Megan C. Marcellin T. Donna Chen Robert J. Riggs Tariq Iqbal Daniel C. Hendrickson David L. Slutzky James H. Lambert Dept. of Systems and Information Engineering University of Virginia United States Coast Guard Charlottesville VA USA University of Virginia Charlottesville VA USA Dept. of Systems and Information Engineering University of Virginia Charlottesville VA USA Dept. of Civil and Environmental Engineering University of Virginia Charlottesville VA USA Asset Management & Special Projects Port of Virginia Norfolk VA USA Fermata LLC and University of Virginia

There is urgency for electrifying fleet vehicles as a means to reach net-zero emissions and promote sustainability, including at maritime container ports. Ports are exploring the incorporation of electric terminal tractors and supporting infras-tructure in an effort to minimize the environmental effects of their operations while simultaneously improving service performance. The challenges include planning of investments in infrastructure that will meet charging requirements of these terminal tractors while maintaining operational efficiencies. This paper develops an optimization and associated risk register for strategic capacity expansion of electric vehicle fleets at maritime container ports. The approach includes multi-criteria decision analysis (MCDA) and a characterization of enterprise risk as a disruption of system order. A demonstration of schedule optimization uses linear program-ming models for thirty-two combinations of plug-in, wireless, and wireless dynamic charging infrastructure configurations to determine optimal charger locations. In a robust ensemble model, the optimization accompanies a comprehensive risk analysis that disrupts importance orders across seven scenarios: (1) Environ-mental Change, (2) Policy Revision, (3) Technology Innovation, (4) Cyber Attack, (5) Market Shift, (6) Electrical Grid Stress, and (7) Workforce Interruption. The results support the decisions and enterprise risk management for a $1.5 billion strategic plan for port infrastructure. The plan involves selecting charging station locations, determining charging schedules, and selecting charger models while considering multiple performance criteria such as safety, operational efficiency, cost-effectiveness, and reliability. The approach is generally applicable for a variety of complex systems to mitigate schedule and cost risks while improving sustainability. The audience of the paper includes owners and operators of transportation and energy infrastructures, asset managers,

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Physics-informed neural networks for phase-resolved data assimilation and prediction of nonlinear ocean waves

arXiv

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arXiv 2025年

作者： Ehlers, Svenja Hoffmann, Norbert Tang, Tianning Callaghan, Adrian H. Cao, Rui Padilla, Enrique M. Fang, Yuxin Stender, Merten Dynamics Group Hamburg University of Technology Hamburg Germany Department of Mechanical Engineering Imperial College London London United Kingdom Department of Engineering Science University of Oxford Oxford United Kingdom Civil and Environmental Engineering Imperial College London London United Kingdom BarcelonaTech Laboratori d'Enginyeria Marítima Universitat Politécnica de Catalunya Barcelona Spain College of Oceanography and Space Informatics China University of Petroleum Qingdao China Cyber-Physical Systems in Mechanical Engineering Technische Universität Berlin Berlin Germany

The assimilation and prediction of phase-resolved surface gravity waves are critical challenges in ocean science and engineering. Potential flow theory (PFT) has been widely employed to develop wave models and numerical techniques for wave prediction. However, traditional wave prediction methods are often limited. For example, most simplified wave models have a limited ability to capture strong wave nonlinearity, while fully nonlinear PFT solvers often fail to meet the speed requirements of engineering applications. This computational inefficiency also hinders the development of effective data assimilation techniques, which are required to reconstruct spatial wave information from sparse measurements to initialize the wave prediction. To address these challenges, we propose a novel solver method that leverages physics-informed neural networks (PINNs) that parameterize PFT solutions as neural networks. This provides a computationally inexpensive way to assimilate and predict wave data. The proposed PINN framework is validated through comparisons with analytical linear PFT solutions and experimental data collected in a laboratory wave flume. The results demonstrate that our approach accurately captures and predicts irregular, nonlinear, and dispersive wave surface dynamics. Moreover, the PINN can infer the fully nonlinear velocity potential throughout the entire fluid volume solely from surface elevation measurements, enabling the calculation of fluid velocities that are difficult to measure experimentally. © 2025, CC BY-SA.

关键词： Gravity waves

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Establishing a regional interdisciplinary resilience center: a bottom-up approach

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Environment systems and Decisions 2023年第2期43卷 191-199页

作者： Selvaratnam, Thinesh Haselbach, Liv Eren-Tokgoz, Berna Gummelt, Ginger Boudreaux, Kyle Williams, Brian D. Pyne, Matthew I. Linkov, Igor Center for Resiliency Lamar University Beaumont 77710 TX United States Department of Civil and Environmental Engineering Lamar University Beaumont 77710 TX United States Department of Industrial and Systems Engineering Lamar University Beaumont 77710 TX United States Department of Sociology Social Work and Criminal Justice Lamar University Beaumont 77710 TX United States Department of Political Science Lamar University Beaumont 77710 TX United States Department of Biology Lamar University Beaumont 77710 TX United States U.S. Army Engineer Research and Development Center Vicksburg 39180 MS United States

Both natural and manmade disasters have severely impacted the region of Southeast Texas over the past few decades, and this has negatively affected the socio-economic well-being of the region. The state of Texas has suffered 200–250 billion dollars in damages from natural and manmade disasters since 2010. Given the region’s strategic importance to the nation’s energy and security, developing resilience knowledge and multi-disaster resilience research focused on issues pertaining to the region is needed. This paper describes the structure and process of building a center for multi-disaster resilience at a regional public university. By utilizing a bottom-up approach, the Center’s mission and design are broadly democratized through the participation of a variety of scholars and various stakeholders with whom they interact. Resilience needs specific to the Southeast Texas region are examined, as is the relationship between resilience and the academic disciplines of the stakeholders involved. The issues of resilience in the region are discussed as well as the future steps for the Center’s continued growth and development for the study of resilience. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

关键词： Disaster resilience Regional resilience Resilience center Resilience indicators

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Explainable Artificial Intelligence-A New Step towards the Trust in Medical Diagnosis with AI Frameworks:A Review

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Computer Modeling in engineering & Sciences 2022年第12期133卷 843-872页

作者： Nilkanth Mukund Deshpande Shilpa Gite Biswajeet Pradhan Mazen Ebraheem Assiri Department of Electronics&Telecommunication LavaleSymbiosis Institute of TechnologySymbiosis International(Deemed University)Pune412115India Electronics&Telecommunication Vilad GhatDr.Vithalrao Vikhe Patil College of EngineeringAhmednagarMaharashtra414111India Centre for AdvancedModelling and Geospatial Information Systems(CAMGIS) School of Civil and Environmental EngineeringFaculty of Engineering&ITUniversity of Technology SydneySydney2007Australia Center of Excellence for Climate Change Research King Abdulaziz UniversityJeddah21589Saudi Arabia Earth Observation Centre Universiti KebangsaanInstitute of Climate ChangeMalaysiaSelangor43600Malaysia Department of Computer Science LavaleSymbiosis Institute of TechnologySymbiosis International(Deemed University)Pune412115India Symbiosis Center for Applied Artificial Intelligence(SCAAI) LavaleSymbiosis International(Deemed University)Pune412115India

Machine learning(ML)has emerged as a critical enabling tool in the sciences and industry in recent ***’s machine learning algorithms can achieve outstanding performance on an expanding variety of complex tasks-thanks to advancements in technique,the availability of enormous databases,and improved computing *** learning models are at the forefront of this ***,because of their nested nonlinear structure,these strong models are termed as“black boxes,”as they provide no information about how they arrive at their *** a lack of transparencies may be unacceptable in many applications,such as the medical domain.A lot of emphasis has recently been paid to the development of methods for visualizing,explaining,and interpreting deep *** situation is substantially different in safety-critical *** lack of transparency of machine learning techniques may be limiting or even disqualifying issue in this ***,when single bad decisions can endanger human life and health(e.g.,autonomous driving,medical domain)or result in significant monetary losses(e.g.,algorithmic trading),depending on an unintelligible data-driven system may not be an *** lack of transparency is one reason why machine learning in sectors like health is more cautious than in the consumer,e-commerce,or entertainment *** is the term introduced in the preceding *** AImodel’s black box nature will become explainable with these *** in the medical domain,diagnosing a particular disease through AI techniques would be less adapted for commercial *** models’explainable natures will help them commercially in diagnosis decisions in the medical *** paper explores the different frameworks for the explainability of AI models in the medical *** available frameworks are compared with other parameters,and their suitability for medical fields is also discussed.

关键词： Medical imaging explainability artificial intelligence XAI

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On the Effects of Airport Capacity Expansion Under Responsive Airlines and Elastic Passenger Demand

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SSRN 2022年

作者： Gong, Zhenwei Zhang, Fangni Liu, Wei Graham, Daniel J. Department of Industrial and Manufacturing Systems Engineering University of Hong Kong Hong Kong Department of Aeronautical and Aviation Engineering The Hong Kong Polytechnic University Hong Kong Centre for Transport Studies Department of Civil and Environmental Engineering Imperial College London United Kingdom

This paper investigates the effect of airport expansion on air traffic and its implications on airport congestion, airline competition and the social welfare, considering different airline market structures (i.e., perfect substitutes market or imperfect substitutes market) and airport administrative regimes (i.e., zero-profit, profit-maximizing, and welfare-maximizing). We develop an analytical tri-level model to examine the air traffic equilibrium in the airport-airline-passenger system, the effect of airport capacity expansion on the traffic equilibrium, and the decisions of different stakeholders, i.e., airport’s decision on flight charge in the first level, airlines' decisions on flight volume and airfare in the second level, and the passenger choice equilibrium in the third level. The analysis in this paper suggests that (i) airport capacity expansion may induce the airline market to over schedule flights and lead to a more congested airport (i.e., capacity paradox);(ii) with a given airport charge, the capacity paradox is more likely to occur in airline market with fewer competitive airlines;(iii) given the same airport capacity and traffic, capacity paradox is more likely to occur under a welfare-maximizing airport operator (compared to zero-profit and profit-maximizing);(iv) airlines with market power will internalize a portion of airport congestion based on their market share, while a leader airline with the knowledge of the follower's response will scale up/down their airfare in order to maximize its profit;(v) under different market structures, capacity expansion always increases the aggregate traffic volume when the airport charge is fixed. © 2022, The Authors. All rights reserved.

关键词： Air transportation

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3D Simulation of Battery Fire on a Large Steel Frame Structure due to Depleted Battery Piles

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Journal of Architectural Environment & Structural engineering Research 2022年第3期5卷 34-51页

作者： Nicole Braxtan Jorge Nunez Shen-En Chen Tiefu Zhao Lynn Harris Dave Cook Department of Civil and Environmental Engineering University of North Carolina at CharlotteCharlotteNC 28223US Department of Electrical and Computer Engineering University of North Carolina at CharlotteCharlotteNC 28223US Deutsche Bahn RaleighNCUS Rail Propulsion Systems FullertonCAUS

Lithium ion batteries(LIB)can rupture and result in thermal runaway and battery *** the process of transporting lithium ion batteries using trains,the massive collection of batteries can cause train fire and pose significant danger to the *** is especially critical when the fire occurs amid a heavily populated metropolitan *** paper reports the 3D analysis of a warehouse with possible train fire due to LIB rupture and the fire propagation at a rail *** critical fire cases with the battery train in close vicinity to the warehouse were *** six fire cases are the worst-case scenarios of a Monte Carlo simulation of different fire cases that may occur to an actual steel storage facility at the Capital Railyard,Raleigh,North Carolina.A 3D finite element(FE)frame model was constructed for the steel warehouse and the most critical fire cases were *** results indicated that several structural components of the warehouse would experience large stresses and deflections during the simulated battery fires and resulting in instability to the ***,members of the roof frame represent the most critical elements and that the members can result in large deformations as early as 4 minutes after the fire ***,effective utilization of fire protection can delay somewhat the fire effects and extend time to failure to 45 minutes and in one of the simulated cases,prevent structural ***,fire from LIB waste transport using train is a very realistic problem due to the thermal runaway,and the analysis performed in current study can be used as a preventive investigation technique for buildings that may be exposed to the train fire risk.

关键词： Lithium ion battery Train fire Propagation Structural safety

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