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arXiv 2023年

作者： Liu, M. Vivienne Reed, Patrick M. Gold, David Quist, Garret Anderson, C. Lindsay Field of Systems Engineering Cornell University IthacaNY14853 United States Civil and Environmental Engineering Cornell University IthacaNY14853 United States Facilities and Campus Service Cornell University IthacaNY14853 United States Field of Systems Engineering Cornell Energy Systems Institute Cornell University IthacaNY14853 United States

The emergence of microgrids (MGs) has provided a promising solution for decarbonizing and decentralizing the power grid, mitigating the challenges posed by climate change. However, MG operations often involve considering multiple objectives that represent the interests of different stakeholders, leading to potentially complex conflicts. To tackle this issue, we propose a novel multi-objective reinforcement learning framework that explores the high-dimensional objective space and uncovers the tradeoffs between conflicting objectives. This framework leverages exogenous information and capitalizes on the data-driven nature of reinforcement learning, enabling the training of a parametric policy without the need for long-term forecasts or knowledge of the underlying uncertainty distribution. The trained policies exhibit diverse, adaptive, and coordinative behaviors with the added benefit of providing interpretable insights on the dynamics of their information use. We employ this framework on the Cornell University MG (CU-MG), which is a combined heat and power MG, to evaluate its effectiveness. The results demonstrate performance improvements in all objectives considered compared to the status quo operations and offer more flexibility in navigating complex operational tradeoffs. © 2023, CC BY-NC-ND.

关键词： Sensitivity analysis

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Machine Learning and Simulation-Based Framework for Disaster Preparedness Prediction

Machine Learning and Simulation-Based Framework for Disaster...

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2021 Winter Simulation Conference, WSC 2021

作者： Jiang, Zhenlong Ji, Ran Chen, Yudi Ji, Wenying George Mason University Department of Systems Engineering and Operations Research FairfaxVA22030 United States George Mason University Department of Civil Environmental and Infrastructure Engineering FairfaxVA22030 United States

ISBN: (纸本)9781665433112

Sufficient preparedness is essential to community resilience following natural disasters. Understanding disaster preparedness of residents in the affected area improves the efficiency and equity of relief operations. This research aims to develop a machine learning and simulation-based approach to predict disaster preparedness using various demographic features from multisource data. The proposed approach comprises four steps: (1) collecting and integrating various data sources, including the FEMA National Household Survey data, US census data, and county-level disaster declaration data;(2) training multiple classification models with the prepared data set and selecting the model with best prediction performance;(3) simulating resident demographic features for at the county level;(4) predicting disaster preparedness status with simulated data for a selected county. A case study is presented to demonstrate the reliability and applicability of the proposed framework. © 2021 IEEE.

关键词： Disaster prevention

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Al-Biruni Earth Radius(BER)Metaheuristic Search Optimization Algorithm

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Computer systems Science & engineering 2023年第5期45卷 1917-1934页

作者： El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Seyedali Mirjalili Nima Khodadad Mona A.Al duailij Amel Ali Alhussan Doaa Sami Khafaga Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Centre for Artificial Intelligence Research and Optimization Torrens University AustraliaFortitude ValleyQLD 4006Australia Yonsei Frontier Lab Yonsei UniversitySeoul03722Korea Department of Civil and Environmental Engineering Florida International UniversityMiamiUSA Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

Metaheuristic optimization algorithms present an effective method for solving several optimization problems from various types of applications and *** metaheuristics and evolutionary optimization algorithms have been emerged recently in the literature and gained widespread attention,such as particle swarm optimization(PSO),whale optimization algorithm(WOA),grey wolf optimization algorithm(GWO),genetic algorithm(GA),and gravitational search algorithm(GSA).According to the literature,no one metaheuristic optimization algorithm can handle all present optimization *** novel optimization methodologies are still *** Al-Biruni earth radius(BER)search optimization algorithm is proposed in this *** proposed algorithm was motivated by the behavior of swarm members in achieving their global *** search space around local solutions to be explored is determined by Al-Biruni earth radius calculation method.A comparative analysis with existing state-of-the-art optimization algorithms corroborated the findings of BER’s validation and testing against seven mathematical optimization *** results show that BER can both explore and avoid local *** has also been tested on an engineering design optimization *** results reveal that,in terms of performance and capability,BER outperforms the performance of state-of-the-art metaheuristic optimization algorithms.

关键词： Metaheuristics evolutionary optimization exploration exploitation mutation Al-biruni earth radius

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Multi-armed Bandit Experimental Design: Online Decision-Making and Adaptive Inference

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Management Science 2025年第6期71卷 4828-4846页

作者： Simchi-Levi, David Wang, Chonghuan Institute for Data Systems and Society Massachusetts Institute of Technology CambridgeMA02139 United States Department of Civil and Environmental Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Operations Research Center Massachusetts Institute of Technology CambridgeMA02139 United States Center for Computational Science and Engineering Massachusetts Institute of Technology CambridgeMA02139 United States

Multi-armed bandit has been well known for its efficiency in online decision-making in terms of minimizing the loss of the participants’ welfare during experiments (i.e., the regret). In clinical trials and many other scenarios, the statistical power of inferring the treatment effects (i.e., the gaps between the mean outcomes of different arms) is also crucial. Nevertheless, minimizing the regret entails harming the statistical power of estimating the treatment effect because the observations from some arms can be limited. In this paper, we investigate the trade-off between efficiency and statistical power by casting the multi-armed bandit experimental design into a minimax multi-objective optimization problem. We introduce the concept of Pareto optimality to mathematically characterize the situation in which neither the statistical power nor the efficiency can be improved without degrading the other. We derive a useful sufficient and necessary condition for the Pareto optimal solutions to the minimax multi-objective optimization problem. Additionally, we design an effective Pareto optimal multi-armed bandit experiment that can be tailored to different levels of the trade-off between the two objectives. Moreover, we extend the design and analysis to the setting where the outcome of each arm consists of an adversarial baseline reward and a stochastic treatment effect, demonstrating the robustness of our design. Finally, motivated by clinical trials, we examine the setting where the employed experiment must split the experimental units into a small number of batches, and we propose a flexible Pareto optimal design. © 2024 INFORMS.

关键词： Multiobjective optimization

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Rethink Biosolids: Risks and Opportunities in the Circular Economy

SSRN

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SSRN 2025年

作者： Xue, Jinkai Liu, Jinyong Xiao, Feng Giesy, John P. Verstraete, Willy Uchida, Yoshitaka Ni, Bing-Jie Li, Zhenyu Kaur, Guneet Liu, Yang McBean, Edward Jiang, Daqian Chibwe, Leah Ng, Kelvin Tsun Wai Environmental Systems Engineering Faculty of Engineering & Applied Science University of Regina ReginaSKS4S 0A2 Canada Department of Chemical & Environmental Engineering University of California RiversideCA92521 United States Department of Civil and Environmental Engineering University of Missouri ColumbiaMO65211 United States Department of Veterinary Biomedical Sciences and Toxicology Centre Western College of Veterinary Medicine University of Saskatchewan SaskatoonSKS7N 5B4 Canada Department of Integrative Biology and Center for Integrative Toxicology Michigan State University East LansingMI48824 United States Department of Environmental Sciences Baylor University Waco76706 United States Ghent University Ghent Belgium Research Faculty of Agriculture Hokkaido University Sapporo060-0809 Japan School of Civil and Environmental Engineering University of New South Wales SydneyNSW2052 Australia P.O. Box 8328 Al-Jubail31951 Saudi Arabia School of Engineering University of Guelph 50 Stone Rd E GuelphONN1G 2W1 Canada School of Civil & Environmental Engineering Queensland University of Technology BrisbaneQLD Australia Department of Civil Construction and Environmental Engineering University of Alabama TuscaloosaAL United States University of Regina ReginaSKS4S 0A2 Canada Environmental Systems Engineering Faculty of Engineering & Applied Science University of Regina ReginaSKS4S 0A2 Canada

Biosolids are a byproduct of wastewater treatment with potential value due to their nutrient, organic matter, and mineral content. However, the traditional use of biosolids in land application (including agricultural applications) can be unsustainable due to the environmental and public health risks posed by contaminants such as per- and poly-fluoroalkyl substances (PFAS), micro-/nano-plastics (MNPs), and antibiotic-resistance genes (ARGs). These pollutants can accumulate in soil, enter ecosystems, and pose long-term risks to food safety and human health, making land applications risky. Through the lens of the circular economy, biosolids should be valorized through holistic, sustainable approaches that prioritize environmental protection and resource recovery. Advanced technologies like anaerobic membrane bioreactors, pyrolysis, and hydrothermal liquefaction can extract valuable resources such as energy and bioplastics while destroying harmful contaminants. In addition, biosolids hold untapped potential as a carbon storage resource, offering a sustainable alternative to land application while mitigating climate change. Such a circular economy strategy minimizes waste, reduces environmental impacts, and unlocks the full potential of biosolids as a renewable resource. © 2025, The Authors. All rights reserved.

关键词： Circular economy

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An Approximate Dynamic Programming Approach to Vehicle Dispatching and Relocation Using Time-Dependent Travel Times

An Approximate Dynamic Programming Approach to Vehicle Dispa...

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International Conference on Intelligent Transportation

作者： Yunping Huang Nan Zheng Enming Liang Shu-Chien Hsu Renxin Zhong Department of Civil and Environmental Engineering The Hong Kong Polytechnic University Hong Kong SAR China Department of Civil Engineering Institute of Transport Studies Monash University Australia School of Data Science City University of Hong Kong Hong Kong SAR China School of Intelligent Systems Engineering Sun Yat-Sen University China

The imbalance between vehicle supply and on-demand customers has been a long-standing challenge for central ride-sourcing platforms. Current literature usually bases the design of dispatching and relocation strategies on the time-independent traffic condition (speed) assumption to reduce the problem dimension while uncertain demand and travel time subject to traffic congestion can significantly affect the optimal solutions. Therefore, we first propose a network-level traffic state estimation algorithm using functional data analysis. Then a multi-stage decision model is proposed to address the matching and repositioning of a centralized platform controlling a fleet of vehicles. Further, the customer spatial-temporal uncertainty is considered under the formulation of a stochastic programming problem. Then, an Approximate Dynamic Programming (ADP) based approach is developed for solving the multi-stage decisions efficiently. Our algorithm is evaluated in a designed simulator based on NYC yellow taxi data and the Manhattan road network. Simulation results show that the total profit can be enhanced compared with traditional time-independent traffic assumption strategies and several decision strategies.

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Multi-behavior Learning for Socially Compatible Autonomous Driving

Multi-behavior Learning for Socially Compatible Autonomous D...

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International Conference on Intelligent Transportation

作者： Sanjula Jayawardana Vindula Jayawardana Kaneeka Vidanage Cathy Wu University of Westminster UK Informatics Institute of Technology Colombo Sri Lanka Department of Electrical Engineering and Computer Science MIT Laboratory for Information & Decision Systems Cambridge MA USA Department of Civil and Environmental Engineering MIT Laboratory for Information Decision Systems Institute of Data Systems and Society Cambridge MA USA

Autonomous vehicles (AVs) have the potential to revolutionize transportation, but their effective integration into the real world requires addressing the challenge of interacting with human drivers. Real-world driving involves negotiating and cooperating with fellow drivers through social cues, necessitating AVs to also demonstrate such social compatibilities. However, despite the popularity, current learning-based control methods for AV policy synthesis often overlook this crucial aspect. In this work, we look at the problem of enabling socially compatible driving when AV control policies are learned. We leverage human driving data to learn a social preference model of human driving and then integrate it with reinforcement learning-based AV policy synthesis using Social Value Orientation theory. In particular, we propose to use multi-task reinforcement learning to learn diverse social compatibility levels in driving (ex: altruistic, prosocial, individualistic, and competitive), focusing on the requirement of having diverse behaviors in real-world driving. Using highway driving scenarios, we demonstrate through experiments that socially compatible AV driving not only enables naturalistic driving behaviors but also reduces collision rates from the baseline. Our findings reveal that without social compatibility, AV policies tend to adopt dangerously competitive driving behaviors, while the incorporation of social compatibility fosters smoother vehicle maneuvers.

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RETRACTED ARTICLE: On the prediction of methane fluxes from pristine tropical peatland in Sarawak: application of a denitrification–decomposition (DNDC) model

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environmental Science and Pollution Research 2022年第20期29卷 30724-30738页

作者： Sa’adi, Zulfaqar Yaseen, Zaher Mundher Muhammad, Mohd Khairul Idlan Iqbal, Zafar Centre for Environmental Sustainability and Water Security (IPASA) School of Civil Engineering Faculty of Engineering Universiti Teknologi Malaysia Sekudai Malaysia Department of Urban Planning Engineering Networks and Systems Institute of Architecture and Construction South Ural State University Chelyabinsk Russia New Era and Development in Civil Engineering Research Group Scientific Research Center Al-Ayen University Thi-Qar Iraq College of Creative Design Asia University Taichung City Taiwan Department of Water and Environmental Engineering School of Civil Engineering Faculty of Engineering Universiti Teknologi Malaysia (UTM) Bahru Johor Malaysia

Tropical peatlands have high potential function as a major source of atmospheric methane (CH4) and can contribute to global warming due to their large soil carbon stock, high groundwater level (GWL), high humidity and high temperature. In this study, a process-based denitrification–decomposition (DNDC) model was used to simulate CH4 fluxes in a pristine tropical peatland in Sarawak. To test the accuracy of the model, eddy covariance tower datasets were compared. The model was validated for the year 2014, which showed the good performance of the model for simulating CH4 emissions. The monthly predictive ability of the model was better than the daily predictive ability, with a determination coefficient (R2) of 0.67, model error (ME) of 2.47, root mean square error (RMSE) of 3.33, mean absolute error (MAE) of 2.92 and mean square error (MSE) of 11.08. The simulated years of 2015 and 2016 showed the good performance of the DNDC model, although under- and overestimations were found during the drier and rainy months. Similarly, the monthly simulations for the year were better than the daily simulations for the year, showing good correlations at R2 at 0.84 (2015) and 0.87 (2016). Better statistical performance in terms of monthly ME, RMSE, MAE and MSE at − 0.11, 3.38, 3.05 and 11.45 for 2015 and − 1.14, 5.28, 4.93 and 27.83 for 2016, respectively, was also observed. Although the statistical performance of the model simulation for daily average CH4 fluxes was lower than that of the monthly average, we found that the results for total fluxes agreed well between the observed and the simulated values (E = 6.79% and difference = 3.3%). Principal component analysis (PCA) showed that CH4, GWL and rainfall were correlated with each other and explained 41.7% of the total variation. GWL was found to be relatively important in determining the CH4 fluxes in the naturally inundated pristine tropical peatland. These results suggest that GWL is an essential input variable for the DNDC mo

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Temporal Transfer Learning for Traffic Optimization with Coarse-grained Advisory Autonomy

arXiv

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arXiv 2023年

作者： Cho, Jung-Hoon Li, Sirui Kim, Jeongyun Wu, Cathy The Department of Civil and Environmental Engineering The Laboratory for Information & Decision Systems Massachusetts Institute of Technology CambridgeMA02139 United States The Institute for Data Systems and Society The Laboratory for Information & Decision Systems Massachusetts Institute of Technology CambridgeMA02139 United States The Laboratory for Information & Decision Systems Massachusetts Institute of Technology CambridgeMA02139 United States The Laboratory for Information & Decision Systems The Institute for Data Systems and Society The Department of Civil and Environmental Engineering Massachusetts Institute of Technology CambridgeMA02139 United States

The recent development of connected and automated vehicle (CAV) technologies has spurred investigations to optimize dense urban traffic to maximize vehicle speed and throughput. This paper explores advisory autonomy, in which real-time driving advisories are issued to the human drivers, thus achieving near-term performance of automated vehicles. Due to the complexity of traffic systems, recent studies of coordinating CAVs have resorted to leveraging deep reinforcement learning (RL). Coarse-grained advisory is formalized as zero-order holds, and we consider a range of hold duration from 0.1 to 40 seconds. However, despite the similarity of the higher frequency tasks on CAVs, a direct application of deep RL fails to be generalized to advisory autonomy tasks. To overcome this, we utilize zero-shot transfer, training policies on a set of source tasks–specific traffic scenarios with designated hold durations–and then evaluating the efficacy of these policies on different target tasks. We introduce Temporal Transfer Learning (TTL) algorithms to select source tasks for zero-shot transfer, systematically leveraging the temporal structure to solve the full range of tasks. TTL selects the most suitable source tasks to maximize the performance of the range of tasks. We validate our algorithms on diverse mixed-traffic scenarios, demonstrating that TTL more reliably solves the tasks than baselines. This paper underscores the potential of coarse-grained advisory autonomy with TTL in traffic flow optimization. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep reinforcement learning

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A mixed-reality pedagogical innovation in the reality of a new normal

A mixed-reality pedagogical innovation in the reality of a n...

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2021 International Foundations Congress and Equipment Expo: From Traditional to Emerging Geotechnics, IFCEE 2021

作者： Bennett, Victoria G. Harteveld, Casper Zastavker, Yevgeniya V. Abdoun, Tarek Hossein, Mohsen Omidvar, Mehdi Wen, Kejun Wirth, Xenia Dept. of Civil and Environmental Engineering Rensselaer Polytechnic Institute TroyNY United States Game Design College of Arts Media and Design Northeastern Univ. BostonMA United States Olin College NeedhamMA United States Dept. of Civil and Urban Engineering New York Univ. BrooklynNY United States Dept. of Civil and Environmental Engineering Manhattan College BronxNY United States Dept. of Civil and Environmental Engineering and Industrial Systems and Technology Jackson State Univ. JacksonMS United States Dept. of Civil and Environmental Engineering California State Univ. Fullerton FullertonCA United States

ISBN: (数字)9780784483428

All of us will never forget the year 2020. Those of us in higher education will perhaps reflect on the Spring 2020 semester as a turning point in our approach to education. To prepare higher education for future crises and to create a more buoyant, elastic, and resilient educational paradigm, it is imperative that we learn, right now, from the COVID-19 crisis. This is of particular importance for engineering education, which relies on experiential pedagogical practices that are incongruent with online learning environments. Since 2014, a multi-institutional interdisciplinary team has been addressing improvements to engineering education through mixed reality gaming, an educational model that integrates traditional classroom experiences (e.g., lectures, laboratory work) with virtual activities (e.g., game-based learning). We posit that a mixed reality gaming educational model may help mitigate the impact COVID-19-like crises. Herein, we reflect on implementations of a mixed-reality gaming activity in an introductory geotechnical engineering course on five campuses during the pivot to remote learning, which will be remembered as the hallmark of the Spring 2020 semester. Ultimately, the data gathered from these implementations will be used to study the effectiveness of mixed reality gaming on student learning outcomes (specifically, the ways in which student and instructor motivation shape and are shaped by this educational model) to understand (1) the role of mixed reality gaming;and (2) instructors' and students' motivational shifts during such crises. © ASCE.

关键词： Computer aided instruction

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