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arXiv 2024年

作者： Martell, Matthew Terry, Nick Sengupta, Ribhu Salazar, Chris Errett, Nicole A. Miles, Scott B. Wartman, Joseph Choe, Youngjun Industrial & Systems Engineering University of Washington SeattleWA United States Environmental & Occupational Health Sciences University of Washington SeattleWA United States Human Centered Design & Engineering University of Washington SeattleWA United States Civil & Environmental Engineering University of Washington SeattleWA United States

Street View Images (SVI) are a common source of valuable data for researchers. Researchers have used SVI data for estimating pedestrian volumes, demographic surveillance, and to better understand built and natural environments in cityscapes. However, the most common source of publicly available SVI data is Google Street View. Google Street View images are collected infrequently, making temporal analysis challenging, especially in low population density areas. Our main contribution is the development of an open-source data pipeline for processing 360-degree video recorded from a car-mounted camera. The video data is used to generate SVIs, which then can be used as an input for temporal analysis. We demonstrate the use of the pipeline by collecting a SVI dataset over a 38-month longitudinal survey of Seattle, WA, USA during the COVID-19 pandemic. The output of our pipeline is validated through statistical analyses of pedestrian traffic in the images. We confirm known results in the literature and provide new insights into outdoor pedestrian traffic patterns. This study demonstrates the feasibility and value of collecting and using SVI for research purposes beyond what is possible with currently available SVI data. Limitations and future improvements on the data pipeline and case study are also discussed. © 2024, CC BY.

关键词： Population statistics

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Submerge, Learn, Succeed: Enhancing K-12 STEM Education Through Experiential Pedagogy in Naval engineering 54

Submerge, Learn, Succeed: Enhancing K-12 STEM Education Thro...

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54th IEEE Frontiers in Education Conference, FIE 2024

作者： Grey, Alexander Hain, Alexandra Maier-Speredelozzi, Valerie Cochran, Christopher Koziatek, Jack Hines, Caleb Stabile, Caroline Houle, Kelly University of Connecticut Dept. of Political Science StorrsCT United States University of Connecticut Dept. of Civil and Environmental Engineering StorrsCT United States University of Rhode Island Dept. of Mechanical Industrial and Systems Engineering KingstonRI United States University of Rhode Island GEMS-Net KingstonRI United States University of Rhode Island Navy-STEM Coalition KingstonRI United States

ISBN: (纸本)9798350351507

This innovative practices paper presents a novel means of integrating naval engineering into K-12 pedagogy. Submarines are central to the U.S. Navy's deterrence strategy. The submarine industrial base (SIB) is facing a dramatic increase in procurement expectations over the following decade;as such, the two regional flagship universities around the largest submarine producer, the University of Connecticut (UConn) and the University of Rhode Island (URI), founded the UConn-URI Navy STEM Coalition to build a comprehensive pipeline to attempt radical new means of integrating technologies crucial to submarine production into K-12 pedagogies. Goals include getting students more interested in learning science, technology, engineering, and math (STEM) topics and more willing to consider careers in naval engineering. Our most innovative pedagogical shift has been integrating these technology lessons through four discrete summer programs for teachers at various points in their careers to embed them in the language and culture of cutting edge naval research taking place at research universities and provide resources for their own STEM modules. © 2024 IEEE.

关键词： Teaching

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A Hybrid Approach for Considering Topography in Graph-Based Optimization of Water Distribution Networks

A Hybrid Approach for Considering Topography in Graph-Based ...

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World environmental and Water Resources Congress 2023: Adaptive Planning and Design in an Age of Risk and Uncertainty

作者： Sitzenfrei, Robert Qiu, Mengning Ostfeld, Avi Savic, Dragan Kapelan, Zoran Unit of Environmental Engineering Univ. of Innsbruck Tirol Innsbruck Austria Technion-Israel Institute of Technology Haifa Israel KWR Water Research Institute Nieuwegein Netherlands Centre for Water Systems Univ. of Exeter Exeter United Kingdom Faculty of Civil Engineering and Geosciences Dept. of Water Management Delft Univ. of Technology Delft Netherlands

ISBN: (数字)9780784484852

ISBN: (纸本)9780784484852

Water distribution networks (WDNs) are a vital component of urban water infrastructure. They transport water from production sites (sources) to spatially distributed consumers (sinks). Multiobjective optimization procedures are often used to minimize construction costs and at the same time maximize the resilience of such systems, which is usually a very computationally expensive task. Recently, highly efficient approaches based on complex network analysis (CNA) have been developed to solve this task more computationally efficiently. With CNA, very large WDNs can be optimized, considering network topology and demand distribution (using, e.g., demand edge betweenness centrality). However, existing CNA approaches do not consider network topography (i.e., height differences between sources and sinks). Comparing design solutions based on CNA with those found by evolutionary algorithms shows that the least-cost CNA design cannot compete with the latter. In this work, a hybrid approach is developed, where low-cost design CNA solutions are evaluated with a hydraulic solver (Epanet2), and subsequently the demand edge betweenness centrality distribution is iteratively altered for nodes with pressure deficits. This enhanced CNA-based optimization is tested on two different large case studies from the literature and shows promising results (2% cost increase). These solutions were obtained using significantly less computational effort (at least factor 1,000 faster), enabling solving very large WDN optimization problems (>150,000 decision variables). © World environmental and Water Resources Congress *** rights reserved

关键词： Multiobjective optimization

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Hybrid Ensemble-Learning Approach for Renewable Energy Resources Evaluation in Algeria

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Computers, Materials & Continua 2022年第6期71卷 5837-5854页

作者： El-Sayed M.El-Kenawy Abdelhameed Ibrahim Nadjem Bailek Kada Bouchouicha Muhammed A.Hassan Basharat Jamil Nadhir Al-Ansari Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansouraEgypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura 35712Egypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansouraEgypt Energies and Materials Research Laboratory Faculty of Sciences and TechnologyUniversity of Tamanghasset10034TamanghassetAlgeria URERMS Centre de Developpement des Energies Renouvelables(CDER)01000AdrarAlgeria Mechanical Power Engineering Department Faculty of EngineeringCairo UniversityGiza12613GizaEgypt Department Computer Sciences Universidad Rey Juan CarlosMostoles28933MadridSpain Department of Civil Environmental and Natural Resources EngineeringLulea University of Technology97187LuleaSweden

In order to achieve a highly accurate estimation of solar energy resource potential,a novel hybrid ensemble-learning approach,hybridizing Advanced Squirrel-Search Optimization Algorithm(ASSOA)and support vector regression,is utilized to estimate the hourly tilted solar irradiation for selected arid regions in ***-term measured meteorological data,including mean-air temperature,relative humidity,wind speed,alongside global horizontal irradiation and extra-terrestrial horizontal irradiance,were obtained for the two cities of Tamanrasset-and-Adrar for two *** computational algorithms were considered and analyzed for the suitability of *** two new algorithms,namely Average Ensemble and Ensemble using support vector regression were developed using the hybridization *** accuracy of the developed models was analyzed in terms of five statistical error metrics,as well as theWilcoxon rank-sum and ANOVA *** the previously selected algorithms,K Neighbors Regressor and support vector regression exhibited good ***,the newly proposed ensemble algorithms exhibited even better *** proposed model showed relative root mean square errors lower than 1.448%and correlation coefficients higher than *** was further verified by benchmarking the new ensemble against several popular swarm intelligence *** is concluded that the proposed algorithms are far superior to the commonly adopted ones.

关键词： Renewable energy resources hybrid modeling tilted solar irradiation arid region

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A peer-to-peer logic environment to validate flashing yellow arrow decision support system

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Journal of Traffic and Transportation engineering(English Edition) 2021年第5期8卷 735-750页

作者： Hatem Abou-Senna Essam Radwan Hesham Eldeeb Center for Advanced Transportation Systems Simulations(CATSS) Department of CivilEnvironmental and Construction Engineering(CECE)University of Central FloridaOrlandoFL 32816USA Deep Numerics LLC.OrlandoFL 32820USA

The flashing yellow arrow(FYA)signal display creates an opportunity to enhance the leftturn phase with a variable mode that can be changed on *** research develops an integrated general purpose data collection module that time stamps detector and phase state changes within a National Electrical Manufacturers Association(NEMA)actuated traffic signal controller to provide recommendations for the flashing yellow arrow left-turn mode on a cycle-by-cycle basis.115 left-turn approaches at 38 intersections with locations across the State of Florida were analyzed totaling 1370 h of video data processed including off-peak and peak *** data extraction was an essential step in developing thegap thresholds for the permissive *** intersection field data were obtained through loop detector mapping to the controller in the lab in real-time mode using a peerto-peer logic environment.A custom communications software was developed to retrieve instantaneous channel input data,synchronize opposing through green phase,analyze traffic information,provide the algorithm decision,and generate a real-time log recording the events to determine whether it would be optimal to switch the red arrow to a flashing yellow *** algorithm determines the time interval between the successive arrivals of vehicles and computes the corresponding headway for each lane by cycle on a secondby-second ***-to-peer logic is a necessary step to verify and validate new traffic concepts prior to field-testing and offers the advantage of acquiring and analyzing realtime traffic data coupled with video feed with the benefit of a safe environment.

关键词： Traffic engineering Peer-to-peer logic Flashing yellow arrow Decision support system Permissive left-turn Four section head

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Phase Re-Service in Reinforcement Learning Traffic Signal Control

Phase Re-Service in Reinforcement Learning Traffic Signal Co...

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International Conference on Intelligent Transportation

作者： Zhiyao Zhang George Gunter Marcos Quinones-Grueiro Yuhang Zhang William Barbour Gautam Biswas Daniel Work Department of Civil and Environmental Engineering Institute for Software Integrated Systems Vanderbilt University USA Institute for Software Integrated Systems Vanderbilt University USA Department of Computer Science Institute for Software Integrated Systems Vanderbilt University USA

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

This article proposes a novel approach to traffic signal control that combines phase re-service with reinforcement learning (RL). The RL agent directly determines the duration of the next phase in a pre-defined sequence. Before the RL agent's decision is executed, we use the shock wave theory to estimate queue expansion at the designated movement allowed for re-service and decide if phase re-service is necessary. If necessary, a temporary phase re-service is inserted before the next regular phase. We formulate the RL problem as a semi-Markov decision process (SMDP) and solve it with proximal policy optimization (PPO). We conducted a series of experiments that showed significant improvements thanks to the introduction of phase re-service. Vehicle delays are reduced by up to 29.95% of the average and up to 59.21% of the standard deviation. The number of stops is reduced by 26.05% on average with 45.77% less standard deviation.

关键词： Shock waves Reinforcement learning Delays Logic Standards Queueing analysis Optimization Intelligent transportation systems

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Modal analysis of blood flows in saccular aneurysms

arXiv

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arXiv 2024年

作者： Nguyen, Thien-Tam Kasperski, Davina Huynh, Phat Kim Le, Trung Quoc Le, Trung Bao Department of Civil Construction and Environmental Engineering North Dakota State University FargoND58102 United States Department of Industrial and Management Systems Engineering University of South Florida TampaFL33620 United States

Currently, it is challenging to investigate aneurismal hemodynamics based on current in-vivo data such as Magnetic Resonance Imaging or Computed Tomography due to the limitations in both spatial and temporal resolutions. In this work, we investigate the use of modal analysis at various resolutions to examine its usefulness for analyzing blood flows in brain aneurysms. Two variants of Dynamic Mode Decomposition (DMD): (i) Hankel-DMD;and (ii) Optimized-DMD, are used to extract the time-dependent dynamics of blood flows during one cardiac cycle. First, high-resolution hemodynamic data in patient-specific aneurysms are obtained using Computational Fluid Dynamics. Second, the dynamics modes, along with their spatial amplitudes and temporal magnitudes are calculated using the DMD analysis. Third, an examination of DMD analyses using a range of spatial and temporal resolutions of hemodynamic data to validate the applicability of DMD for low-resolution data, similar to ones in clinical practices. Our results show that DMD is able to characterize the inflow jet dynamics by separating large-scale structures and flow instabilities even at low spatial and temporal resolutions. Its robustness in quantifying the flow dynamics using the energy spectrum is demonstrated across different resolutions in all aneurysms in our study population. Our work indicates that DMD can be used for analyzing blood flow patterns of brain aneurysms and is a promising tool to be explored in in-vivo. © 2024, CC BY-NC-SA.

关键词： Modal analysis

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STRATEGIC TAKT PLANNING 33rd

STRATEGIC TAKT PLANNING

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33rd Annual Conference of the International Group for Lean Construction, IGLC 2025

作者： Tommelein, Iris D. Emdanat, Samir Civil and Environmental Engineering Department Director Project Production Systems Laboratory University of California Berkeley United States President and Co-Founder Planner DearbornMI United States

Reports on the use of takt tend to focus on applications aimed at so-called operational takt planning. Instead, this paper focuses on strategic takt planning. Strategic- and operational takt planning must go hand in hand. Specifically, strategic takt planning is required upfront in project delivery as it informs prospective contractors what is expected of them regarding their capacity allocation, and supply chains must be lined up to meet the crews’ just-in-time delivery needs to meet customer demand. Without a strategic takt plan that spells out assumptions embodied in the procurement schedule, it may be difficult (without later requiring contract adjustments) to onboard specialty contractors and suppliers who are then willing and able to jointly develop their operational takt plans and execute their work accordingly. This paper describes how, during strategic takt planning, project execution strategies can be explored based on alternative choices of a takt and the number of takt zones, and how plan options can be evaluated without requiring many project specifics. The computation and exploration of the merits of alternative options contribute to the body of knowledge on takt planning. They lead to better alignment between contracting- and operational strategies when maximizing flow is the objective. © 2025, International Group for Lean Construction. All rights reserved.

关键词： Strategic planning

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Variance Reduction for Score Functions Using Optimal Baselines

arXiv

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arXiv 2022年

作者： Keane, Ronan Gao, H. Oliver Systems Science and Engineering Cornell University United States Civil and Environmental Engineering Cornell University United States

Many problems involve the use of models which learn probability distributions or incorporate randomness in some way. In such problems, because computing the true expected gradient may be intractable, a gradient estimator is used to update the model parameters. When the model parameters directly affect a probability distribution, the gradient estimator will involve score function terms. This paper studies baselines, a variance reduction technique for score functions. Motivated primarily by reinforcement learning, we derive for the first time an expression for the optimal state-dependent baseline, the baseline which results in a gradient estimator with minimum variance. Although we show that there exist examples where the optimal baseline may be arbitrarily better than a value function baseline, we find that the value function baseline usually performs similarly to an optimal baseline in terms of variance reduction. Moreover, the value function can also be used for bootstrapping estimators of the return, leading to additional variance reduction. Our results give new insight and justification for why value function baselines and the generalized advantage estimator (GAE) work well in practice. Copyright © 2022, The Authors. All rights reserved.

关键词： Reinforcement learning

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Intelligent modelling of clay compressibility using hybrid meta-heuristic and machine learning algorithms

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Geoscience Frontiers 2021年第1期12卷 441-452页

作者： Pin Zhang Zhen-Yu Yin Yin-Fu Jin Tommy HTChan Fu-Ping Gao Department of Civil and Environmental Engineering The Hong Kong Polytechnic UniversityHung HomKowloonHong KongChina School of Civil Engineering&Built Environment Science and Engineering FacultyQueensland University of Technology(QUT)BrisbaneQld4001Australia Key Laboratory for Mechanics in Fluid Solid Coupling Systems Institute of MechanicsChinese Academy of SciencesBeijing100190China China School of Engineering Science University of Chinese Academy of SciencesBeijing100049China

Compression index Ccis an essential parameter in geotechnical design for which the effectiveness of correlation is still a *** paper suggests a novel modelling approach using machine learning(ML)*** performance of five commonly used machine learning(ML)algorithms,***-propagation neural network(BPNN),extreme learning machine(ELM),support vector machine(SVM),random forest(RF)and evolutionary polynomial regression(EPR)in predicting Cc is comprehensively investigated.A database with a total number of 311 datasets including three input variables,*** void ratio e0,liquid limit water content wL,plasticity index Ip,and one output variable Cc is first *** algorithm(GA)is used to optimize the hyper-parameters in five ML algorithms,and the average prediction error for the 10-fold cross-validation(CV)sets is set as thefitness function in the GA for enhancing the robustness of ML *** results indicate that ML models outperform empirical prediction formulations with lower prediction *** yields the lowest error followed by BPNN,ELM,EPR and *** the ranges of input variables in the database are large enough,BPNN and RF models are recommended to predict ***,if the distribution of input variables is continuous,RF model is the best ***,EPR model is recommended if the ranges of input variables are *** predicted correlations between input and output variables using five ML models show great agreement with the physical explanation.

关键词： Compressibility Clays Machine learning Optimization Random forest Genetic algorithm

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