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arXiv 2024年

作者： Haywood-Alexander, Marcus Arcieri, Giacomo Kamariotis, Antonios Chatzi, Eleni Department of Civil Environmental and Geomatic Engineering ETH Zürich Wolfgang-Pauli Strasse Zürich8049 Switzerland Future Resilient Systems Singapore-ETH Centre Singapore

The accurate modelling of structural dynamics is crucial across numerous engineering applications, such as Structural Health Monitoring (SHM), seismic analysis, and vibration control. Often, these models originate from physics-based principles and can be derived from corresponding governing equations, often of differential equation form. However, complex system characteristics, such as nonlinearities and energy dissipation mechanisms, often imply that such models are approximative and often imprecise. This challenge is further compounded in SHM, where sensor data is often sparse, making it difficult to fully observe the system’s states. To address these issues, this paper explores the use of Physics-Informed Neural Networks (PINNs), a class of physics-enhanced machine learning (PEML) techniques, for the identification and estimation of dynamical systems. PINNs offer a unique advantage by embedding known physical laws directly into the neural network’s loss function, allowing for simple embedding of complex phenomena, even in the presence of uncertainties. This study specifically investigates three key applications of PINNs: state estimation in systems with sparse sensing, joint state-parameter estimation, when both system response and parameters are unknown, and parameter estimation within a Bayesian framework to quantify uncertainties. The results demonstrate that PINNs deliver an efficient tool across all aforementioned tasks, even in presence of modelling errors. However, these errors tend to have a more significant impact on parameter estimation, as the optimization process must reconcile discrepancies between the prescribed model and the true system behavior. Despite these challenges, PINNs show promise in dynamical system modeling, offering a robust approach to handling uncertainties. © 2024, CC BY.

关键词： Vibration analysis

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Geospatial Analysis of Extreme Temperature Impacts in Agricultural systems Using Machine Learning

Geospatial Analysis of Extreme Temperature Impacts in Agricu...

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IEEE International Symposium on systems engineering (ISSE)

作者： Rayshaun L. Wheeler John S. Miller Gia N. Smith James H. Lambert Cody A. Pennetti Garrick E. Louis Deandre A. Johnson Department of Civil & Environmental Engineering University of Virginia Charlottesville VA USA School of Data Science University of Virginia Charlottesville VA USA Department of Systems & Information Engineering University of Virginia Charlottesville VA USA Department of Engineering & Society University of Virginia Charlottesville VA USA

ISBN: (数字)9798350353723

ISBN: (纸本)9798350353730

Recent higher temperatures, observed compared to previous years, have led to more frequent and intense heat waves, adversely affecting agriculture and human health. This paper explores using Inverse Distance Weighting (IDW) and Kriging to develop GIS layers for a heat-based risk index. Recent increases in global mean temperatures have led to more frequent and intense heat waves, impacting food supply, vegetation, and human health. Optimal plant growth temperatures in temperate climates range from 20°C to 30°C, with nighttime temperatures also being critical. Exceeding these temperature ranges can reduce plant immunity, cause disease, and result in poor crop yields. For example, mango cultivars need a "safe" temperature range of 10°C to 12°C to avoid damage. The intensification of higher temperatures established July 2023 record the hottest days on Earth, highlighting the need for effective monitoring and management tools. IDW, a deterministic method, offers a straightforward and efficient approach by assuming closer points have more influence. Kriging, a probabilistic geostatistical technique, accounts for spatial autocorrelation and prediction uncertainty, making it suitable for complex datasets. The study compares these methods' performance in creating continuous surfaces representing heat-based risk. IDW is simple and efficient but less accurate in sparsely populated areas, while Kriging is more accurate and detailed but requires more computational resources and expertise. An automated workflow for data preprocessing, model training, and cross-validation ensures consistent results. The study recommends adopting IDW for initial assessments and transitioning to Kriging as expertise grows. Enhanced data collection on temperature and population density is also advised to improve GIS layer accuracy, aiding better decision-making and resource allocation to mitigate extreme heat effects on agriculture and public health.

关键词： Heating systems Support vector machines Training Temperature distribution Accuracy Computational modeling Decision making Linear regression Resource management Random forests Climate change

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PreMixer: MLP-Based Pre-training Enhanced MLP-Mixers for Large-scale Traffic Forecasting

arXiv

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arXiv 2024年

作者： Zhang, Tongtong Cui, Zhiyong Wang, Bingzhang Ren, Yilong Yu, Haiyang Deng, Pan Wang, Yinhai State Key Laboratory of Intelligent Transportation Systems Beihang University Beijing100191 China Civil and Environmental Engineering University of Washington SeattleWA98195 United States School of Software Engineering Beihang University Beijing100083 China

In urban computing, precise and swift forecasting of multivariate time series data from traffic networks is crucial. This data incorporates additional spatial contexts such as sensor placements and road network layouts, and exhibits complex temporal patterns that amplify challenges for predictive learning in traffic management, smart mobility demand, and urban planning. Recently, large-scale traffic between cities and urban agglomerations has increasingly become a routine part of daily life. Consequently, there is an increasing need to forecast traffic flow across broader geographic regions and for higher temporal coverage. However, current research encounters limitations because of the inherent inefficiency of model and their unsuitability for large-scale traffic network applications due to model complexity. This paper proposes a novel framework, named PreMixer, designed to bridge this gap. It features a predictive model and a pre-training mechanism, both based on the principles of Multi-Layer Perceptrons (MLP). The PreMixer comprehensively consider temporal dependencies of traffic patterns in different time windows and processes the spatial dynamics as well. Additionally, we integrate spatio-temporal positional encoding to manage spatiotemporal heterogeneity without relying on predefined graphs. Furthermore, our innovative pre-training model uses a simple patch-wise MLP to conduct masked time series modeling, learning from long-term historical data segmented into patches to generate enriched contextual representations. This approach enhances the downstream forecasting model without incurring significant time consumption or computational resource demands owing to improved learning efficiency and data handling flexibility. Our framework achieves comparable state-of-the-art performance while maintaining high computational efficiency, as verified by extensive experiments on large-scale traffic datasets. These results underscore our contributions to the traffic predict

关键词： Mixers (machinery)

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Real-Time Activity Duration Extraction of Crane Works for Data-Driven Discrete Event Simulation

Real-Time Activity Duration Extraction of Crane Works for Da...

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Simulation Winter Conference

作者： Manuel Jungmann Lucian Ungureanu Timo Hartmann Hector Posada Rolando Chacon Civil Systems Engineering Technische Universität Berlin Berlin GERMANY Civil and Environmental Engineering Universitat Politècnica de Catalunya Barcelona SPAIN

ISBN: (纸本)9781665476621

The construction industry is struggling with low productivity rates because of a low level of digitalization, dynamic interactions, and uncontrollable circumstances on sites, which make the planning process complex. Usage of the digital twin construction paradigm enables to facilitate construction management and leverage the sector's unexploited potential. This research addresses current shortcomings by real-time discrete event simulation. During crane operations, kinematic data were collected, which were classified by machine learning algorithms for activity recognition and duration extraction. Based on the identified durations, Goodness-of-Fit techniques determined suitable probability density functions. The resulting probability density functions were used as input parameters in stochastic discrete event simulations. It was shown that with enriched data collection, probability density functions have to be updated. The data-driven discrete event simulation facilitates decision-making processes by providing more reliable real-time information for the planning of upcoming construction works. Thus, data-based instead of experience-based management can be enabled.

关键词： Productivity Cranes Decision making Kinematics Probability density function Data collection Real-time systems

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Anaerobic Digestion of Whole Cells and Post-Extracted Algae Residues Of *** Using Immobilized Batch Reactor

SSRN

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SSRN 2023年

作者： Abimbola, Tobi Christodoulatos, Christos Lawal, Adeniyi New Jersey Center for Microchemical Systems Department of Chemical Engineering and Materials Science Stevens Institute of Technology HobokenNJ07030 United States Center for Environmental Systems Department of Civil Environmental and Ocean Engineering Stevens Institute of Technology HobokenNJ07030 United States

Conventional anaerobic digesters are operated by mixing microbes with substrates under anaerobic conditions to generate biomethane (bioCH4). After anaerobic digestion (AD), digestates are inseparable from spent microbes and in some cases, the digester fails due to the accumulation of organic acids. This study demonstrates an immobilized bacteria reactor system adopting digested sludge (DS) encapsulated in calcium-alginate to digest algae and post-extracted algae residue (PEAR). This has not been reported elsewhere. From this study, it was established that a mixing speed of 100rpm and 10 days of AD are the best conditions for stable encapsulation of DS, while at a concentration of 4% w/v sodium alginate, bioCH4 was maximized. Relative to the conventional approach, 24% and 58% more bioCH4 was produced, digesting algae and PEAR of ***, respectively. Spent encapsulated DS was reused and a performance of 85% was achieved, based on the bioCH4 production employing spent encapsulated DS compared to fresh encapsulated DS. Improved bioCH4 production from the immobilized reactor suggests that alga-to-biofuel can be more economically viable while separability of digestates at the end of AD indicates a reduction in waste generation and digestates can be processed for other uses such as animal feed or fertilizer. © 2023, The Authors. All rights reserved.

关键词： Algae

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POTENTIAL OF BIODIESEL PRODUCTION IN THE UNITED ARAB EMIRATES BASED ON THE LOCALLY AVAILABLE FEEDSTOCK 50

POTENTIAL OF BIODIESEL PRODUCTION IN THE UNITED ARAB EMIRATE...

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50th International Conference on Computers and Industrial engineering: Sustainable Digital Transformation, CIE 2023

作者： Asaad, Sara Maen Inayat, Abrar Almallahi, Maryam Nooman Ghenai, Chaouki Shanableh, Abdallah Elgendi, Mahmoud Biomass and Bioenergy Research Group Center for Sustainable Energy and Power Systems Research Research Institute of Sciences and Engineering University of Sharjah Sharjah27272 United Arab Emirates Department of Industrial Engineering and Engineering Management University of Sharjah Sharjah27272 United Arab Emirates Department of Sustainable and Renewable Energy Engineering University of Sharjah Sharjah27272 United Arab Emirates Department of Mechanical and Aerospace Engineering College of Engineering United Arab Emirates University Al Ain United Arab Emirates Department of Civil and Environmental Engineering University of Sharjah Sharjah27272 United Arab Emirates Department of Mechanical Power Engineering and Energy Faculty of Engineering Minia University Minia Egypt National Water and Energy Center United Arab Emirates University P.O. Box 15551 Al Ain United Arab Emirates

ISBN: (纸本)9781713886952

This paper outlines the potential for biodiesel production in the United Arab Emirates (UAE) and discusses locally available feedstock. With a growing global demand for renewable energy sources and sustainable practises, the UAE, like many other countries, is attempting to lessen its reliance on fossil fuels. As a promising renewable resource, biomass provides a possibility for biodiesel production, contributing to the country's long-term energy and sustainability goals. Statistics related to feedstock available in UAE are discussed along with some experimental and modelling studies related to biodiesel production in the UAE. In addition, existing biodiesel production companies in the UAE, such as Lootah Biofuels and Neutral Fuels, and their contributions in helping the UAE reach its sustainability goals are showcased. Finally, future research areas related to biodiesel production and possible feedstock are discussed. © 2023 Computers and Industrial engineering. All rights reserved.

关键词： Biodiesel

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Using penetrometer in situ and in box-core testing to obtain design information for lazy wave riser-soil interaction 5th

Using penetrometer in situ and in box-core testing to obtain...

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5th International Symposium on Cone Penetration Testing, CPT 2022

作者： Safaqah, O. Low, H.E. Pant, S. Ingarfield, S. Bransby, M.F. Randolph, M.F. Westgate, Z.J. ExxonMobil HoustonTX United States Fugro Netherlands Centre for Offshore Foundation Systems Oceans Graduate School The University of Western Australia Australia Department of Civil and Environmental Engineering University of Massachusetts Amherst United States

ISBN: (纸本)9781032312590

Geotechnical assessment of vertical seabed stiffness during small vertical cycles of riser movement is important for riser fatigue analysis. These stiffness values change both with the amplitude of the cycles (because of non-linear stiffness) but also as the seabed consolidates under the small amplitude loading during long-term operation. To encapsulate both effects in design, a series of site-specific small displacement ball penetrometer testing was conducted both at seabed and in box-core samples on deck during a geotechnical survey for a recent deep water project. The paper describes the cyclic load and displacement controlled in situ testing, the results obtained, and how this data aided design. Of particular note were the long duration cyclic tests which were carried out for sufficiently long so as to not only capture secant stiffness reduction due to remoulding, but also subsequent stiffness increases due to consolidation. This novel test program performed on intact soil (as opposed to reconstituted samples in the laboratory or geotechnical centri-fuge) confirms that consolidation hardening can occurs in naturally structured near-seabed soils. © 2022 the Author(s).

关键词： Cyclic loads

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Quantifying the Capability of Autonomous Robot-Assisted Distribution in Last-Mile: Insight from Rural to Urban Configurations

Quantifying the Capability of Autonomous Robot-Assisted Dist...

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International Conference on Intelligent Transportation

作者： Yubin Liu Keyang Zhang Jose Escribano-Macias Panagiotis Angeloudis Department of Civil and Environmental Engineering Transport Systems & Logistics Lab Centre for Transport Studies Imperial College London South Kensington London UK

The surge in online transactions and e-commerce has promoted the demand for timely parcel delivery, increasing the burden on traditional manual delivery. This presents an opportunity for deploying autonomous robot-assisted delivery to improve operation efficiency where robots can navigate autonomously through road networks and deliver packages to customers. This research evaluates the performance of robots in executing last-mile deliveries from rural to urban regions characterised by varying demand density and road congestion. We establish a mixed integer programming model to present the robot-assisted delivery scenario and investigate the impact of key operating parameters on delivery performance. To reduce the computational complexity, we introduced a service set and variable reduction technique. Finally, we validate our model on data sets from rural to urban areas and benchmark the results with various classic delivery modes. Results demonstrate that robot-assisted delivery outperforms the existing delivery modes regarding delivery efficiency in urban regions with an average improvement rate of 76.51%, by shortening vehicle travel and parcel sorting time.

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A review on the charging station planning and fleet operation for electric freight vehicles

arXiv

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arXiv 2023年

作者： Alam, Md Rakibul Guo, Zhaomiao Department of Civil Environmental and Construction Engineering Resilient Intelligent and Sustainable Energy Systems Cluster University of Central Florida FL32766 United States

Freight electrification introduces new opportunities and challenges for planning and operation. Although research on charging infrastructure planning and operation is widely available for general electric vehicles, unique physical and operational characteristics of Electric Freight Vehicles (EFVs) coupled with specific patterns of logistics require dedicated research. This paper presents a comprehensive literature review to gain a better understanding of the state-of-the-art research efforts related to planning (charging station siting and sizing) and operation (routing, charge scheduling, platoon scheduling, and fleet sizing) for EFVs. We classified the existing literature based on the research topics, innovations, methodologies, and solution approaches, and future research directions are identified. Different types of methodologies, such as heuristic, simulation, and mathematical programming approaches, were applied in the reviewed literature where mathematical models account for the majority. We further narrated the specific modeling considerations for different logistic patterns and research goals with proper reasoning. To solve the proposed models, different solution approaches, including exact algorithms, metaheuristic algorithms, and software simulation, were evaluated in terms of applicability, advantages, and disadvantages. This paper helps to draw more attention to the planning and operation issues and solutions for freight electrification and facilitates future studies on EFV to ensure a smooth transition to a clean freight system. © 2023, CC BY.

关键词： Fleet operations

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Sensitivity of Snow Magnitude and Duration to Hydrology Model Parameters

SSRN

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SSRN 2024年

作者： Singh, Bhupinderjeet Ferdousi, Tanvir Abatzoglou, John Swarup, Samarth Adam, Jennifer Rajagopalan, Kirti Department of Biological Systems Engineering Washington State University United States Biocomplexity Institute University of Virginia CharlottesvilleVA United States Management of Complex Systems University of California MercedCA United States Department of Civil and Environmental Engineering Washington State University United States

Process-based hydrology models are critical for understanding streamflow and water supply under global change. However, these models require parameterization which introduces additional uncertainty into the models. The role that these parameters play in driving uncertainty is under-studied, especially for intermediary processes outside of streamflow. An important example in snowmelt dominant regions are intermediary processes related to snowpack accumulation and ablation. We examine the sensitivity of snow magnitude and duration to eleven parameters relevant to snow processes in the coupled crop-hydrology model VIC-CropSyst using a hybrid global-local Distributed Evaluation of Local Sensitivity Analysis approach. With the Pacific Northwest US as a case study, our specific research questions are: (a) What is the sensitivity response of peak snow water equivalent (SWE) and snow duration in the global parameter space? (b) What are the key drivers of the sensitivity response? and (c) Which of the most sensitive parameters can we immediately improve by leveraging existing data products? Both target variables were sensitive to less than four of the eleven parameters. We found that modeled peak SWE was most sensitive to either the precipitation partitioning temperature threshold or the albedo of new snow, depending on the geography and associated interplay between hydro-meteorological factors. In contrast, snow duration was primarily sensitive to the albedo of new snow and the albedo decay coefficient during snowmelt. Machine learning workflows applied on the sensitivity response explained the model behavior and determined key geographic and hydro-meteorological drivers of the sensitivity response. Regions where the timing of significant precipitation directly overlaid with temperature exposures close to freezing exhibited higher sensitivity of peak SWE to precipitation partitioning. In contrast, much colder high-elevation regions that lack this overlay and have a delayed

关键词： Snow

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