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Enhancing Deep Learning-Based Damage Segmentation with Depth Hallucination 14

Enhancing Deep Learning-Based Damage Segmentation with Depth...

14th International Workshop on Structural Health Monitoring: Designing SHM for Sustainability, Maintainability, and Reliability, IWSHM 2023

作者： Mondal, Tarutal Ghosh Jahanshahi, Mohammad R. Department of Civil Architectural and Environmental Engineering Missouri University of Science and Technology RollaMO United States Lyles School of Civil Engineering Elmore Family School of Electrical and Computer Engineering Purdue University West LafayetteIN United States

ISBN: (纸本)9781605956930

Recent studies have suggested that the fusion of cross-modal information can enhance the performance of deep learning-based segmentation algorithms. In this context, this study evaluates the benefits of RGB-D fusion with regard to damage segmentation in reinforced concrete buildings. The fusion of depth data was observed to enhance the segmentation performance significantly. Additionally, a number of surrogate techniques based on modality hallucination and monocular depth estimation are exploited to eliminate the need for depth sensing at test time without foregoing the benefits of depth fusion. The proposed techniques require depth data only for network training, and at test time, depth features are simulated from the corresponding RGB frames, obliterating the need for real depth perception. The proposed methods are evaluated and are shown to increase the damage segmentation accuracy. © 2023 by DEStech Publi cations, Inc. All rights reserved

关键词： Deep learning

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An Impact of Oversight in Trasmission Portion in the Hybrid Smart Grid Systems

An Impact of Oversight in Trasmission Portion in the Hybrid ...

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Pareek, Ravi Kant Ravela, Ratna Raju Mandal, Sanjeev Kumar Vivekananda Global University Department of Civil Engineering Jaipur India Noida Institute of Engineering & Technology Department of Electrical and Electronics Engineering Uttar Pradesh Greater Noida India Computer Science Engineering Department Bangalore India

ISBN: (纸本)9798350357769

Establishing a solid basis for Smart Grids requires the cooperation of Wide Area Meter Systems (WAMS), state estimation, and technology for communication and information (ICT). Although the accuracy and dependability of power grid information are improved by these technologies, their integration raises certain possible security risks that need careful consideration. Because of the complex interaction between ICT and the power system, new approaches are needed to model the intelligent energy system as a Cyberspace Physical System and analyze the effects of ICT errors on the electrical grid's operations. This research presents a novel methodological technique to evaluate the reliability of a 5G-based WAMS by using numerical computing and modeling with Stochastic Activity Networks. The research takes into account both external issues, such rain impacts, and internal ones, like component failures, and assesses how these affect the WAMS's capacity to provide reliable data for power network condition estimates. A 5G-based WAMS shows near-ideal performance in comparison to other state estimate methodologies and weather circumstances, highlighting its potential for use in Smart Grid maintenance applications in the future. © 2023 IEEE.

关键词： 5G-enabled Communication Failures Dependability Assessment and Renewable Energy Integration ICT Risks Power System Operations Smart Grids State Estimation Stochastic Activity Networks Wide Area Meter Systems (WAMS)

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Stability and Uncertainty Propagation in Power Networks: A Lyapunov-based Approach with applications to Renewable Resources Allocation

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IEEE Transactions on Power Systems 2025年

作者： Kazma, Mohamad H. Tah, Ahmad F. Vanderbilt University Civil & Environmental Engineering and Electrical & Computer Engineering Departments NashvilleTN37235 United States

The rapid increase in the integration of intermittent and stochastic renewable energy resources (RER) introduces challenging issues related to power system stability. Interestingly, identifying grid nodes that can best support stochastic loads from RER has gained recent interest. Methods based on Lyapunov stability are commonly exploited to assess the stability of power networks. These strategies approach quantifying system stability while considering: (i) simplified reduced order power system models that do not model power flow constraints, or (ii) data-driven methods that are prone to measurement noise and hence can inaccurately depict stochastic loads as system instability. In this paper, while considering a nonlinear differential algebraic equation (NL-DAE) model, we introduce a new method for assessing the impact of uncertain renewable power injections on the stability of power system nodes/buses. The identification of stable nodes informs the operator/utility on how renewables injections affect the stability of the grid. The proposed method is based on optimizing metrics equivalent to the Lyapunov spectrum of exponents;its underlying properties result in a computationally efficient and scalable stable node identification algorithm for renewable energy resources allocation. The developed framework is studied on various standard power networks. © 1969-2012 IEEE.

关键词： Electric power system stability

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Digital Twins for Photorealistic Event-Based Structural Dynamics 41st

Digital Twins for Photorealistic Event-Based Structural Dyna...

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41st IMAC, A Conference and Exposition on Structural Dynamics, 2023

作者： Davis, Allison Walker, Edward Chen, Marcus Felipe, Moises Mascareñas, David Moreu, Fernando Cattaneo, Alessandro Department of Electrical Engineering and Computer Science College of Engineering and Applied Science University of Wyoming LaramieWY United States Department of Mechanical Engineering George R. Brown School of Engineering Rice University HoustonTX United States Department of Electrical and Computer Engineering College of Engineering University of Washington SeattleWA United States Los Alamos National Laboratory Los AlamosNM United States Department of Civil Construction and Environmental Engineering University of New Mexico AlbuquerqueNM United States

ISBN: (纸本)9783031349096

Digital twins are virtual representations of real-world structures that can be used for modeling and simulation. Because of digital twins’ ability to simulate complex structural behaviors, they also have potential for structural health monitoring (SHM) applications. Video-based SHM techniques are advantageous due to the lower installation/maintenance costs, analysis in high-spatial resolution, and its non-contact monitoring features. Both digital twins and video-based techniques hold particular interest in the fields of non-destructive evaluation, damage identification, and modal analysis. An effective use of these techniques for SHM applications still poses several challenges. Neural radiance fields (NeRFs) are an emerging and promising type of neural network that can render photorealistic novel views of a complex scene using a sparse data set of 2D images. Originally, NeRF was designed to capture static scenes, but recent work has extended its capability to capture dynamic scenes which has implications for medium and long-term SHM. However, to date, most NeRFs use frame-based images and videos as input data. Frame-based video monitoring approaches result in redundant information derived from the fact that, for structural dynamics monitoring, only a small number of active pixels record the actual dynamical changes in the structure, resulting in intensive computational loads for data processing and storage. A promising alternative is event-based imaging, which only records pixel-wise changes on the illumination of a scene. Event-based imaging creates a sparse set of data, while accurately capturing the dynamics. The work proposes a method to extract the dynamics of a structure using a generated digital twin. Using Unreal Engine 5, digital twins of rigid and non-rigid structures were generated. The digital twin model was then used along with an event-based camera simulator to generate event-based data. A frequency analysis framework was then developed to extract the

关键词： Cameras

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Integrated deep learning and Bayesian optimization approach for enhanced prediction of high-performance concrete strength

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Asian Journal of civil engineering 2025年第6期26卷 2371-2390页

作者： Tipu, Rupesh Kumar Goyal, Archna Singh, Digvijay Kumar, Ayyala Kishore Ajay Department of Civil Engineering School of Engineering & Technology K. R. Mangalam University Sohna Haryana Gurugram 122103 India Department of Computer Science & Engineering School of Engineering & Technology K. R. Mangalam University Sohna Haryana Gurugram 122103 India Department of Electrical Engineering School of Engineering & Technology K. R. Mangalam University Sohna Haryana Gurugram 122103 India

Prediction of high performance concrete (HPC) compressive strength is very important for determination of mix design optimization and structural reliability. An integrated deep learning framework, together with a Bayesian optimization for improving accuracy in prediction, is what this study presents. Finally, the optimized DNN yielded a coefficient of determination (R2) of 0.932 and the lowest RMSE (4.104 MPa) among all the proposed regression models, and was superior to the traditional regression models, including linear regression (RMSE = 10.089 MPa) and kernel ridge regression (RMSE = 10.095 MPa). Hyperparameters were fine tuned using a Bayesian optimization reducing the RMSE from 10.985 MPa (unoptimized DNN) down to 4.104 MPa. SHAP-based feature importance analysis revealed that age and cement content were the most influential variables, reinforcing domain knowledge about cement hydration and strength development. In addition, a graphical user interface (GUI) was built to make practical implementation possible and thus allow real time compressive strength prediction using material proportions. In summary, it shows that a deep learning combined with hyperparameter optimization is able to greatly improve the predictive reliability and efficiency of the HPC strength, which facilitates the sustainable construction practices and efficient material utilization. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Bayesian optimization Compressive strength prediction Deep neural networks High-performance concrete SHAP analysis

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Characterization of Concrete Curing Using a Microwave Harmonic Sensor

Characterization of Concrete Curing Using a Microwave Harmon...

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2023 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2023

作者： Uluer, Ismail Chopperla, Krishna Siva Teja Yirenya-Tawiah, Daniel Kwabena Isgor, Burkan Weiss, William Jason Weller, Tom School of Electrical Engineering and Computer Science Oregon State University CorvallisOR United States School of Civil and Construction Engineering Oregon State University CorvallisOR United States

ISBN: (纸本)9781665442282

The paper describes a process to use a microwave harmonic sensor, or frequency doubling reflectenna, to understand the evolution of water content during concrete curing. The harmonic sensor has a 3D form factor, is constructed from printed circuit board pieces, and operates at 1.2 GHz (receive) and 2.4 GHz (transmit). The system operates by interrogating a sensor that is embedded in the concrete under test using a wireless reader and correlating variation in signal strength received back from the sensor with the complex permittivity of the concrete. Empirical results from tests of sand-water mixtures, sand-ionic solution mixtures, and cement mortar mixtures are presented. The data demonstrate high sensitivity in signal strength versus moisture and ionic content for relatively low, realistic moisture conditions. © 2023 IEEE.

关键词： Ionic strength

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High-Rate Structural Health Monitoring: Part-III Algorithms 1

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41st IMAC, A Conference and Exposition on Structural Dynamics, 2023

作者： Laflamme, Simon Hu, Chao Department of Civil Construction and Environmental Engineering Department of Electrical and Computer Engineering Iowa State University AmesIA United States Department of Mechanical Engineering University of Connecticut StorrsCT United States

ISBN: (数字)9783031349461

ISBN: (纸本)9783031349454

The real-time estimation of the structural health of high-rate systems, termed "high-rate structural health monitoring" (HRSHM), is critical in designing decision mechanisms that can ensure structural integrity and performance. However, this is a difficult task, because three aspects uniquely characterize these systems: (1) large uncertainties in the external loads, (2) high levels of nonstationarities and heavy disturbances, and (3) unmodeled dynamics generated from changes in system configurations. In addition, because these systems are experiencing events of high amplitudes (often beyond 100 gn) over short durations (under 100 ms), a successful feedback mechanism is one that can operate under 1 ms. A solution to the unique system characteristics and temporal constraint is the design and application of real-time learning algorithms. Part III of this III part tutorial will provide attendees with an organized discussion of challenges and opportunities in designing real-time learning algorithms for HRSHM applications. In particular, we will explore fast real-time learning for time-series prediction using conventional machine learning and deep learning algorithms, with an emphasis on crafting the input space to improve algorithmic efficiency, and discuss a path to rapid real-time state estimation. © 2023, The Society for Experimental Mechanics, Inc.

关键词： Time series

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A Customizable Neural Network Based Framework for Autonomous Vehicle Control with Human-Guided Learning 27

A Customizable Neural Network Based Framework for Autonomous...

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27th IEEE International Conference on Intelligent Transportation Systems, ITSC 2024

作者： Li, Tianyi Wang, Shian Shang, Mingfeng Choi, Seongjin Stern, Raphael University of Minnesota Department of Civil Environmental and Geo-Engineering MinneapolisMN55455 United States Saint Louis University Department of Civil Engineering Saint LouisMO63103 United States The University of Texas at El Paso Department of Electrical and Computer Engineering EI PasoTX79968 United States

ISBN: (纸本)9798331505929

This study introduces a novel control framework for adaptive cruise control (ACC) in autonomous vehicles (AVs), utilizing Long Short-Term Memory (LSTM) networks and physics-informed constraints. The LSTM component captures complex vehicle dynamics and temporal dependencies, while physics constraints ensure realistic operational limits. This framework supports customization of control objectives, allowing for the integration of various performance metrics to achieve specific goals such as reducing speed variation and enhancing traffic flow. A distinctive feature of this framework is the implementation of human-guided learning, where a human-controlled lead vehicle profile informs the training process. This human-in-the-loop approach allows the controller to adapt to real-world driving patterns and complexities effectively. Extensive simulations demonstrate the framework's adaptability across different scenarios, highlighting its capability to maintain safe vehicle spacing, ensure smooth speed profiles, and optimize energy efficiency. The results highlight the controller's ability to maintain safe spacing while ensuring smooth speed profiles and optimizing energy efficiency, depending on the chosen customization settings. This research paves the way for the development of more intelligent, safe, and efficient ACC systems that can be tailored to various driving conditions and user preferences while benefiting from the knowledge and experience of human drivers during the learning process. © 2024 IEEE.

关键词： Adaptive cruise control

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Developing Computational Intelligence Curriculum Materials to Advance Student Learning for Robot Control and Optimization

Developing Computational Intelligence Curriculum Materials t...

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2024 ASEE Annual Conference and Exposition

作者： Lei, Tingjun Sellers, Timothy Luo, Chaomin Bi, Zhuming Jan, Gene Eu Department of Electrical and Computer Engineering Mississippi State University United States Department of Civil and Mechanical Engineering Purdue University Fort Wayne United States National Taipei University Tainan National University of the Arts Taiwan

The integration of nature-inspired intelligence in computational intelligence curricula, particularly for robot path planning optimization, represents a significant advancement in both research and education realms. This study introduces a unique pedagogical approach that combines sparrow-dissection and scaffolding with flipped learning (SDS-FL) and ongoing project-based methods. This approach is implemented in a graduate-level course where students explore various nature-inspired algorithms such as particle swarm optimization, genetic algorithms, and bat algorithms, provided with source codes for practical application and adaptation. The flipped classroom model ensures pre-class preparation, enabling students to build foundational knowledge independently. In-class sessions are then focused on collaborative project work and discussions, fostering problem-solving, critical thinking, and analytical skills. This hybrid teaching method, promoting active engagement and practical application, is evaluated for its effectiveness through various assessments, including homework, exams, and feedback surveys. The positive outcomes from this innovative pedagogical approach affirm its success in enhancing students' understanding and application of nature-inspired intelligence in computational intelligence, making the learning process more dynamic and impactful. © American Society for engineering Education, 2024.

关键词： Particle swarm optimization (PSO)

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Radio resource management and path planning in intelligent transportation systems via reinforcement learning for environmental sustainability

Radio resource management and path planning in intelligent t...

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International Conference on Innovation and Technological Advances for Sustainability, ITAS 2023

作者： Norouzi, S. Azarasa, N. Abedi, M.R. Mokari, N. Seyedabrishami, S.E. Saeedi, H. Jorswieck, E.A. Department of Electrical and Computer Engineering Tarbiat Modares University Tehran Iran Department of Civil and Environmental Engineering Tarbiat Modares University Tehran Iran College of Engineering Doha University for Science and Technology Doha Qatar Institute of Communications Technology TU Braunschweig Lower Saxony Germany

ISBN: (纸本)9781032803722

Efficient and dynamic path planning has become an important topic for urban areas with larger density of connected vehicles (CV) which results in reduction of travel time and directly contributes to environmental sustainability through reducing energy consumption. CVs exploit the cellular wireless vehicle-to-everything (C-V2X) communication technology to disseminate the vehicle-to-infrastructure (V2I) messages to the Base-station (BS) to improve situation awareness on urban roads. In this paper, we investigate radio resource management (RRM) in such a framework to minimize the age of information (AoI) so as to enhance path planning results. We use the fact that V2I messages with lower AoI value result in less error in estimating the road capacity and more accurate path planning. Through simulations, we compare road travel times and volume over capacity (V/C) against different levels of AoI and demonstrate the promising performance of the proposed framework. © 2025 The Author(s).

关键词： Resource allocation

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