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SSRN 2024年

作者： Liu, Wei Lai, Zhilu Stoura, Charikleia D. Bacsa, Kiran Chatzi, Eleni Department of Industrial Systems Engineering and Management National University of Singapore Singapore Department of Civil Environmental and Geomatic Engineering ETH Zürich Zürich Switzerland Future Resilient Systems Singapore-ETH Centre Singapore Guangzhou China

In the context of structural and industrial asset monitoring and twinning, the estimation of unknown inputs — typically reflecting the loads acting onto a system — stands as a critical factor in ensuring both the safety and performance of engineered systems. This research introduces a novel approach for inferring such unknown inputs from observed outputs, focusing particularly on dynamical systems, whether characterized by known or learned dynamics. Our proposed approach is situated within the framework of Imitation Learning from Observation (ILfO), which is here recast in the context of dynamical systems' estimation. Our primary objective is to infer estimates of input signals on the basis of real-world measured outputs. The problem is formulated as a Partially Observable Markov Decision Process (POMDP), and we address it by utilizing established planning algorithms specifically tailored for ILfO applications. To address the POMDP, we harness the effectiveness and robustness of the cross-entropy method. We first verify the efficacy of the proposed approach on simulated case studies involving dynamical systems possessing well-defined dynamics. The proposed method is further validated on a practical scenario involving experimental data from a scaled wind turbine blade, leveraging dynamics learned through the Neural Extended Kalman Filter (Neural EKF) technique;an approach which leverages deep learning for inferring the dynamics of a system when this is imprecisely known. These examples demonstrate the flexibility of the proposed approach for use with systems featuring a different degree of prescribed dynamics. © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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Impact of Poultry Litter Biochar on Soil Fertility and Radish Growth: environmental Health and Water Quality Considerations

Impact of Poultry Litter Biochar on Soil Fertility and Radis...

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World environmental and Water Resources Congress 2025

作者： Yulai Yang Seong W. Lee Sam Oludayo Alamu Terine Lambert Dong Hee Kang Dept. of Civil and Environmental Engineering Morgan State Univ. Baltimore MD United States Dept. of Industrial and Systems Engineering Morgan State Univ. Baltimore MD United States

ISBN: (数字)9780784486184

ISBN: (纸本)9780784486184

Excessive poultry litter biochar (PLB) is a promising soil amendment for improving soil quality and enhancing carbon sequestration. However, understanding the effects of different PLB production conditions and application rates on plant growth and soil health is still limited. This study investigates the impact of pyrolysis temperature, bedding materials type in poultry litter, and biochar application rates on soil fertility and radish (Raphanus sativus L.) growth. The experiment was conducted in a greenhouse, featuring 12 treatments with four replications. Treatments varied by pyrolysis temperatures (300°C and 500°C), bedding materials (pure poultry litter, 10% pine shavings, and 10% rice hulls), and application rates (2% and 5% by weight). An un-amended soil treatment was included as a control. Soil fertility changes were evaluated. The findings indicated that biochar-amended soils exhibited significant enrichment in essential nutrients, such as nitrogen, phosphorus, and potassium (NPK), leading to enhanced radish growth. Two percent application rate show better results compared with 5%. These results demonstrate that PLB can play a critical role in soil fertility enhancement while repurposing poultry litter waste. This research highlights the environmental health benefits of incorporating solid waste-derived biochar into soil management practices, aligning with sustainable practices that address the lifecycle of solid waste management.

关键词： Waste management

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Determination of Route-based Toll of Urban Expressway with Ant Colony Optimization

Determination of Route-based Toll of Urban Expressway with A...

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International Symposium on Soft Computing and Intelligent systems (SCIS)

作者： Takamasa Akiyama Hiroaki Inokuchi Rei Kawasaki Department of Civil Environmental and Applied Systems Engineering Kansai University Suita Japan Department of Civil Environmental and Applied Systems Engineering Graduate School of Kansai University Suita Japan

The route-based toll for urban expressway is proposed to produce the efficient traffic condition. Ant colony optimization is applied to the combinatorial programing problem for route-based toll determination in urban ... 详细信息

关键词： Ant colony optimization

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Contextual Online Decision Making with Infinite-Dimensional Functional Regression

arXiv

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arXiv 2025年

作者： Hu, Haichen Ai, Rui Bates, Stephen Simchi-Levi, David Center for Computational Science and Engineering MIT United States Institute for Data Systems and Society MIT United States Department of EECS MIT United States Department of Civil and Environmental Engineering MIT United States

Contextual sequential decision-making problems play a crucial role in machine learning, encompassing a wide range of downstream applications such as bandits, sequential hypothesis testing and online risk control. These applications often require different statistical measures, including expectation, variance and quantiles. In this paper, we provide a universal admissible algorithm framework for dealing with all kinds of contextual online decision-making problems that directly learns the whole underlying unknown distribution instead of focusing on individual statistics. This is much more difficult because the dimension of the regression is uncountably infinite, and any existing linear contextual bandits algorithm will result in infinite regret. To overcome this issue, we propose an efficient infinite-dimensional functional regression oracle for contextual cumulative distribution functions (CDFs), where each data point is modeled as a combination of context-dependent CDF basis functions. Our analysis reveals that the decay rate of the eigenvalue sequence of the design integral operator governs the regression error rate and, consequently, the utility regret rate. Specifically, when the eigenvalue sequence exhibits a polynomial decay of order γ1 ≥ 1, the utility regret is bounded by (formula presented) By setting γ = 0, this recovers the existing optimal regret rate for contextual bandits with finite-dimensional regression and is optimal under a stronger exponential decay assumption. Additionally, we provide a numerical method to compute the eigenvalue sequence of the integral operator, enabling the practical implementation of our framework. © 2025, CC BY.

关键词： Distribution functions

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Voting Classifier and Metaheuristic Optimization for Network Intrusion Detection

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Computers, Materials & Continua 2023年第2期74卷 3183-3198页

作者： Doaa Sami Khafaga Faten Khalid Karim Abdelaziz A.Abdelhamid El-Sayed M.El-kenawy Hend K.Alkahtani Nima Khodadadi Mohammed Hadwan Abdelhameed Ibrahim Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt The Department of Civil and Environmental Engineering Florida International UniversityMiamiFLUSA Department of Information Technology College of ComputerQassim UniversityBuraydah51452Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt

Managing physical objects in the network’s periphery is made possible by the Internet of Things(IoT),revolutionizing human *** attacks and unauthorized access are possible with these IoT devices,which exchange data to enable remote *** attacks are often detected using intrusion detection methodologies,although these systems’effectiveness and accuracy are *** paper proposes a new voting classifier composed of an ensemble of machine learning models trained and optimized using metaheuristic *** employed metaheuristic optimizer is a new version of the whale optimization algorithm(WOA),which is guided by the dipper throated optimizer(DTO)to improve the exploration process of the traditionalWOA *** proposed voting classifier categorizes the network intrusions robustly and *** assess the proposed approach,a dataset created from IoT devices is employed to record the efficiency of the proposed algorithm for binary attack *** dataset records are balanced using the locality-sensitive hashing(LSH)and Synthetic Minority Oversampling Technique(SMOTE).The evaluation of the achieved results is performed in terms of statistical analysis and visual plots to prove the proposed approach’s effectiveness,stability,and *** achieved results confirmed the superiority of the proposed algorithm for the task of network intrusion detection.

关键词： Voting classifier whale optimization algorithm dipper throated optimization intrusion detection internet-of-things

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Real-Time Monitoring and Controlling of Water Quality in Water Distribution Networks Based on Flow Cytometry and Fluorescence Spectroscopy

Real-Time Monitoring and Controlling of Water Quality in Wat...

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World environmental and Water Resources Congress 2022: Adaptive Planning and Design in an Age of Risk and Uncertainty

作者： Schuster, Jonas Kadinski, Leonid Cao, Hao Abhijith, Gopinathan R. Grieb, Anissa Li, Pu Ostfeld, Avi Ernst, Mathias Institute of Water Resources and Water Supply Hamburg Univ. of Technology Hamburg Germany Faculty of Civil and Environmental Engineering Technion-Institute of Technology Haifa Israel Institute of Automation and Systems Engineering Technical Univ. Ilmenau Ilmenau Germany

ISBN: (数字)9780784484258

ISBN: (纸本)9780784484258

Guaranteeing the high-quality of water in water distribution networks (WDSs) is a priority when it comes to ensuring public health. Since the majority of German water utilities (and also other EU countries) do not chlorinate, controlling of nutrients in WDSs such as assimilable organic carbon (AOC) and monitoring of microbiological activities is indispensable. Conventional methods to characterize microbiological activity and dissolved organic matter are time-consuming and labor-intensive. This study presents data on flow cytometry and fluorescence spectroscopy as leading-edge technologies for real-time analysis of drinking water quality in WDS. Flow cytometry is a sensitive method which can be applied in online modus for accurate detection of bacterial cell numbers. Furthermore, fluorescence spectroscopy is a rapid and quantitative technique for detailed characterization of dissolved organic carbon (DOC) including fractions of natural organic matter (NOM). The integration of both techniques is promising for real-time water quality analysis and as a supporting tool for quality control. In the initial step of this research, an experimental laboratory setup of simultaneous analysis is developed. Thus, the goal is to achieve knowledge about possible relations between water quality parameters, more precisely bacterial regrowth potential and the character of dissolved organic constituents. In continuous measurements, the initial state of microbiological growth is to be determined based on flow cytometric data. Due to fluorescence spectroscopy data the microbiological regrowth potential will be predicted. This will be achieved by online detecting of certain organic fractions of the DOC which can be utilized as nutrients by present bacteria. The overall project goal is the establishing of an interface between monitoring parameters and water quality simulation models for the WDSs. First promising results show the successful utilization of adapted AOC as regrowth potential pa

关键词： Nutrients

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Advancing remote sensing and machine learning-driven frameworks for groundwater withdrawal estimation in Arizona: Linking land subsidence to groundwater withdrawals

Advancing remote sensing and machine learning-driven framewo...

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作者： Majumdar, Sayantan Smith, Ryan Conway, Brian D. Lakshmi, Venkataraman Department of Civil and Environmental Engineering Colorado State University Fort CollinsCO United States Arizona Department of Water Resources PhoenixAZ United States Department of Engineering Systems and Environment University of Virginia CharlottesvilleVA United States

Groundwater plays a crucial role in sustaining global food security but is being over-exploited in many basins of the world. Despite its importance and finite availability, local-scale monitoring of groundwater withdrawals required for sustainable water management practices is not carried out in most countries, including the United States. In this study, we combine publicly available datasets into a machine learning framework for estimating groundwater withdrawals over the state of Arizona. Here we include evapotranspiration, precipitation, crop coefficients, land use, annual discharge, well density, and watershed stress metrics for our predictions. We employ random forests to predict groundwater withdrawals from 2002 to 2020 at a 2 km spatial resolution using in situ groundwater withdrawal data available for Arizona Active Management Areas (AMA) and Irrigation Non-Expansion Areas (INA) from 2002 to 2009 for training and 2010–2020 for validating the model respectively. The results show high training ((Formula presented.)) and good testing ((Formula presented.)) scores with normalized mean absolute error (NMAE) ≈ 0.62 and normalized root mean square error (NRMSE) ≈ 2.34 for the AMA/INA region. Using this method, we spatially extrapolate the existing groundwater withdrawal estimates to the entire state and observe the co-occurrence of both groundwater withdrawals and land subsidence in South-Central and Southern Arizona. Our model predicts groundwater withdrawals in regions where production wells are present on agricultural lands and subsidence is observed from Interferometric Synthetic Aperture Radar (InSAR), but withdrawals are not monitored. By performing a comparative analysis over these regions using the predicted groundwater withdrawals and InSAR-based land subsidence estimates, we observe a varying degree of subsidence for similar volumes of withdrawals in different basins. The performance of our model on validation datasets and its favourable comparison with i

关键词： Forecasting

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Prediction of LWST values using ML and ANN techniques from frcition and texture measuring devices based on long term field data

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International Journal of Transportation Science and Technology 2025年

作者： Alsheyab, Mohammad Ahmad Khasawneh, Mohammad Ali Alotaibi, Danah Hussain Mohammed, Nayeemuddin Khasawneh, Ahmad Ali Graduate Research Assistant of Iowa State University Department of Civil Construction and Environmental Engineering AmesIA United States Civil Engineering Department Prince Mohammad Bin Fahd University Al Khobar31952 Saudi Arabia Management Engineering and Process Improvement Department/Integrated Systems Engineering Department Ohio State University Wexner Medical Center & Ohio State University ColumbusOH43202 United States

Given the cost, time requirements, and limited accessibility of Locked Wheel Skid Trailer (LWST) testing, this research explores alternative approaches to predicting skid number (SN) using more affordable and readily available devices. These alternatives offer quicker results, which is particularly relevant given the standardized nature of skid number measurements worldwide. The study focuses on predicting the (SN) generated by the LWST test through analyzing data from Dynamic Friction Tester (DFT), British Pendulum Tester (BPT), and Circular Texture Meter (CTM) using statistical and machine learning techniques. The research includes a thorough assessment of various prediction methods and factors influencing skid number accuracy, including strategies for handling missing data. Prediction models employ Multiple Linear Regression (MLR), Support Vector Machine (SVM), Artificial Neural Network (ANN), and Ensemble Bagged Tree (EBT) techniques, utilizing predictors such as Dynamic Friction Number at 20 km/h (DFT20), Dynamic Friction Number at 64 km/h (DFT64), British Pendulum Number (BPN), and Mean Profile Depth (MPD). The development of forty-eight models, including Full, Reduced, and individual predictor models, involved employing data imputation methods and stepwise regression. Significant correlations were observed among friction parameters, with DFT20 showing the highest correlation with skid numbers, while MPD exhibited the lowest. Following data imputation, notable enhancements in BPN's correlation were noted. The study emphasizes the superiority of Full models in skid number prediction accuracy, with EBT models, especially those enhanced by data imputation, demonstrating outstanding performance. Statistical simulations confirmed the reliability of these models, indicating minimal outliers, overfitting, and high accuracy in skid number estimation. The prioritization of DFT over BPT and CTM is highlighted, with the potential for further exploration of devices to imp

关键词： Pendulums

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Deception for Advantage in Connected and Automated Vehicle Decision-Making Games*

Deception for Advantage in Connected and Automated Vehicle D...

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IEEE Symposium on Intelligent Vehicle

作者： Hangyu Li Heye Huang Xiaotong Sun Xiaopeng Li Department of Civil & Environmental Engineering University of Wisconsin-Madison Madison United States Intelligent Transportation Thrust Systems Hub The Hong Kong University of Science and Technology (Guangzhou) Guangzhou China Department of Civil and Environmental Engineering The Hong Kong University of Science and Technology Hong Kong SAR China

ISBN: (数字)9798350348811

ISBN: (纸本)9798350348828

Connected and Automated Vehicles (CAVs) have the potential to enhance traffic safety and efficiency. In contrast, aligning both vehicles’ utility with system-level interests in scenarios with conflicting road rights is challenging, hindering cooperative driving. This paper advocates a game theory model, which strategically incorporates deceptive information within incomplete information vehicle games, operating under the premise of imprecise perceptions. The equilibria derived reveal that CAVs can exploit deceptive strategies, not only gaining advantages that undermine the utility of the other vehicle in the game but also posing hazards to the overall benefits of the transportation system. Vast experiments were conducted, simulating diverse inbound traffic conditions at an intersection, validating the detrimental impact on efficiency and safety resulting from CAVs with perception uncertainties, and employing deceptive maneuvers within connected and automated transportation systems. Finally, the paper proposes feasible solutions and potential countermeasures to address the adverse consequences of deception in connected and automated transportation systems. It concludes by calling for integrating these insights into future research endeavors and pursuing to fully realize the potential and expectations of CAVs in enhancing the whole traffic performance.

关键词： Robust control Uncertainty Intelligent vehicles Roads Vehicular ad hoc networks Games Machine learning

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Data-Driven Machine Learning Models for Prediction of Partitioning Behavior of Per- and Poly-Alkyl Substances (Pfas) on Liquid-Solid Interface for Carbon- and Mineral-Based Surfaces

SSRN

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SSRN 2024年

作者： Patel, Harsh V. Park, Hyo-Shin Zhao, Renzun Department of Civil Architectural and Environmental Engineering North Carolina Agricultural and Technical State University GreensboroNC27411 United States Department of Engineering Management and Systems Engineering Old Dominion University NorfolkVA23529 United States

The distribution coefficient (Kd) measures the ratio of concentration adsorbed on solid surfaces to that in the liquid phase, crucial for understanding Per- and Poly-Fluoroalkyl Substances (PFAS) mobility. Despite its importance, machine learning hasn't been fully utilized to predict precise Kd values for PFAS on different materials, which could streamline experimental work. A machine learning model to predict Log Kd for PFAS compounds can help reduce the time and cost-intensive experimental work needed. This study introduces a computational model to predict log Kd for PFAS on various surfaces, addressing associated challenges. Utilizing 24 algorithms, including decision trees, on carbon- and mineral-based adsorbents, the model achieved promising results. R-squared values for decision trees reached 0.86 and 0.90 for carbon- and mineral-based adsorbents, respectively, indicating strong fitting. Further enhancements included feature selection, transformation, and hyperparameter optimization, notably improving accuracy. The optimized decision tree model boasted impressive R-squared values of 0.99 and 0.95 for carbon- and mineral-based adsorbents, respectively. However, challenges such as insufficient data and non-standardized testing procedures persist, hindering the full potential of machine learning in PFAS distribution prediction. Addressing these challenges is crucial for advancing the application of machine learning in PFAS research. © 2024, The Authors. All rights reserved.

关键词： Carbon

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