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Geotechnical Frontiers 2025: Foundations, Retaining Structures, and Geosynthetics

作者： Hasheminezhad, Araz Ceylan, Halil Kim, Sunghwan Tutumluer, Erol Dept. of Civil Construction and Environmental Engineering Program for Sustainable Pavement Engineering and Research Iowa State Univ. AmesIA United States Institute for Transportation Iowa State Univ. AmesIA United States Dept. of Civil and Environmental Engineering Univ. of Illinois at Urbana-Champaign UrbanaIL United States

ISBN: (数字)9780784485972

This paper presents recent findings from an ongoing research study to evaluate the effectiveness of a geosynthetic made from recycled plastic for stabilizing unpaved roads. In the state of Iowa, USA, two full-scale test sections were constructed: one stabilized with a newly developed composite geosynthetic, and the other serving as a control. This innovative geocomposite combines a geogrid made from recycled plastic and a nonwoven geotextile. Following construction, these unpaved test sections were exposed to real traffic loads that included trucks and implements of husbandry (farm equipment). The performance of reinforced unpaved roads was assessed using Lightweight Deflectometer (LWD) tests and field California Bearing Ratio (CBR) data collected using a clegg impact soil tester (CIST). The results indicated that unpaved roads stabilized with the geocomposite significantly reduced permanent deformation during the evaluation period. These findings suggest good potential of the geocomposite for enhancing unpaved road performance, pending further validation through continuous monitoring and evaluation of the test sections over longer periods. © ASCE.

关键词： Soil testing

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Parameter Identification of Signal Attenuation for High Speed Rail Communication System Using Grey Box Neural Network 8

Parameter Identification of Signal Attenuation for High Spee...

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8th International Conference on Instrumentation, Control, and Automation, ICA 2023

作者： Lukman, Selvi Nazaruddin, Yul Yunazwin Joelianto, Endra Institut Teknologi Bandung Engineering Physics Doctoral Program Faculty of Industrial Technology Bandung Indonesia Institut Teknologi Bandung Instrumentation Control Research Group Department of Engineering Physics Bandung Indonesia National Center for Sustainable Transportation Technology Bandung Indonesia

ISBN: (纸本)9798350301274

A reliable connected digital railways must face a huge challenge of network and frequency planning in order to deploy a dedicated infrastructure for High Speed Rail (HSR). This paper investigates the parameter identification of signal attenuation for HSR in a free space propagation case. A neural network based optimization of Grey Box Neural Network (GBNN) is utilized to validate the parameter identification results. Satisfying results of minimum convergences of RMSE = 1.6371 and MAPE = 0.015 have been achieved. Furthermore, the ability of identified parameter in adjusting the dynamic scenario of the real signal attenuation measurement dataset is displayed when the distance is 118.45 meter and the path loss value is 116.82 dB. Through this applied neural network based technique, network designers are able to plan and design the best possible of network performances and frequency planning for HSR. © 2023 IEEE.

关键词： Parameter estimation

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Analysis of Travel Behavior Under Flooding Condition Based on Probe Data in Ubon Ratchathani City, Thailand 2nd

Analysis of Travel Behavior Under Flooding Condition Based o...

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2nd International Conference of Construction, Infrastructure, and Materials, ICCIM 2021

作者： Tsumita, Noriyasu Miyamura, Kohga Jaensirisak, Sittha Fukuda, Atsushi Program of Transportation Systems Engineering Nihon University College of Science and Technology Narashinodai Funabashi-shi2748501 Japan Department of Civil Engineering Faculty of Engineering Ubon Ratchathani University Ubon Ratchathani34190 Thailand Department of Transportation Systems Engineering Nihon University College of Science and Technology Narashinodai Funabashi-shi2748501 Japan

ISBN: (纸本)9789811679483

Recently, urban floods have frequently occurred and severely affected urban activities in developing cities. To minimize the impacts of transport from urban flooding, it is essential to implement adaptation measures in the transport field. Firstly, it is crucial to understand the impacts of travel behaviors and traffic states from urban flooding. Therefore, this study focused on the relationship between travel speed and inundation depth and evaluating adaptation measures in the transport field. The relationship between travel speed and inundation depth was analyzed based on the probe data. A traffic assignment model was developed to evaluate the raising arterial road measure as an adaptation measure in the transport field while reflecting the analysis results of relationships between travel speed and inundation depth. This found that the implementation in raising arterial road measure for the western bypass could be an effective adaptation measure by reducing Vehicle Kilometer Traveled (VKT) by 65% while decreasing the number of detoured by 5000 vehicles compared to the flooding situation without taking any adaptation measures. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Probes

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Optimal Gantry Crane PID Controller Based on LQR with Prescribed Degree of Stability by Means of GA, PSO, and SA 7

Optimal Gantry Crane PID Controller Based on LQR with Prescr...

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7th International Conference on Electric Vehicular Technology, ICEVT 2022

作者： Bandong, Steven Kirana, Rizky Cahya Nazaruddin, Yul Yunazwin Joelianto, Endra Institut Teknologi Bandung Engineering Physics Doctoral Program Faculty of Industrial Technology Bandung Indonesia Institut Teknologi Bandung National Center for Sustainable Transportation Technology Instrumentation and Control Graduate Program Bandung Indonesia Institut Teknologi Bandung National Center for Sustainable Transportation Technology Instrumentation and Control Research Group Department of Engineering Physics Bandung Indonesia

ISBN: (数字)9781665409421

ISBN: (纸本)9781665409421

Trade between islands and countries is increasing in the current era of globalization which also increases the traffic of goods at ports. Rubber Tyred Gantry Crane (RTGC) is an important component in the seaports distribution chain, which act as a loading and unloading machine at the container yard. However, heavy trade traffic will likely cause fatigue and negligence if the RTGC is operated manually. Therefore, it is necessary to automate RTGC by applying optimal control. The paper introduces an alternative approach to designing an optimal PID controller built from the LQR method combined with a prescribed degree of stability for achieving the required transient and steady-state responses of RTGC in the port. Genetic Algorithm (GA), Particle Swarm optimization (PSO), and Simulated Annealing (SA) are applied to select the suitable stability degree value and weighting matrices in the LQR cost function. Simulation results indicate that GA can provide the optimal PID controller to follow the reference trajectory and minimize the swing angle better than PSO and SA. © 2022 IEEE.

关键词： Genetic algorithms

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Integration of Small Unmanned Aircraft Systems and Deep Learning for Efficient Airfield Pavement Crack Detection and Assessment

Integration of Small Unmanned Aircraft Systems and Deep Lear...

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International Conference on transportation and Development 2024: transportation Safety and Emerging Technologies, ICTD 2024

作者： All Sourav, Md Abdullah Ceylan, Halil Kim, Sunghwan Brynick, Matthew Dept. of Civil Construction and Environmental Engineering Iowa State Univ. AmesIA United States Dept. of Civil Construction and Environmental Engineering and Program for Sustainable Pavement Engineering and Research Iowa State Univ. AmesIA United States Program for Sustainable Pavement Engineering and Research Institute for Transportation Iowa State Univ. AmesIA United States Federal Aviation Administration Airport Pavement R&D Section William J. Hughes Technical Center NJ United States

ISBN: (纸本)9780784485514

Airfield pavement inspection and maintenance are critical aspects of aviation infrastructure, representing a substantial portion of life-cycle costs. Longitudinal, transverse, and diagonal (LTD) cracks;corner breaks;shattered slabs in Portland cement concrete (PCC) pavement;and longitudinal and transverse (L&T) cracks of asphalt concrete (AC) pavement consist of most of the airfield pavement distresses. Traditional airfield pavement inspection methods are manual, time-consuming, laborious, and reliant on the inspector’s experience, leading to increased expenses and safety risks. This research explores the potential to automatically identify those distresses in red-green-blue images using four variants of deep learning (DL) model YOLOv8, ranging from nano to large. YOLOv8 is a widely used off-the-shelf DL object detection model that allows rapid training and easy execution. A DL training dataset of 5,273 small uncrewed aircraft systems (sUAS) collected images was developed. The transfer learning technique was used, and the dataset passed through each model 100 times for adequate training. The model exhibits mean average precision values exceeding 0.65, with varying processing times. Such accuracy showed that crack-related distress detection using DL models could enhance airfield pavement inspection efficiency. © ASCE.

关键词： Drones

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Facilitating Emergency Vehicle Passage in Congested Urban Areas Using Multi-agent Deep Reinforcement Learning

arXiv

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arXiv 2025年

作者： Su, Haoran University of California Berkeley United States Transportation Systems program Tandon School of Engineering New York University United States C2SMARTER Center Tier-1 University Transportation Research Center United States Department of Transportation United States

Emergency Response Time (ERT) serves as a pivotal metric of urban resiliency and public safety, encapsulating a city’s capacity to respond promptly and effectively to medical, fire, and crime emergencies. Over the last decade, increasing urbanization and traffic congestion have severely exacerbated ERT, undermining emergency management systems and public trust. In New York City (NYC), average ERT for medical emergencies has surged by 72% from 7.89 minutes in 2014 to 14.27 minutes in 2024, with over 50% of delays attributable to prolonged Emergency Vehicle (EMV) travel times. These escalating delays have dire consequences: every minute of delay during a stroke can lead to the loss of 2 million brain cells, and survival rates for cardiac arrest diminish by 7–10% per minute. This underscores the urgency of addressing EMV travel inefficiencies through advanced routing and traffic management strategies. This dissertation responds to these critical challenges by advancing the state of EMV facilitation through three substantive contributions. First, it introduces EMVLight, a decentralized multi-agent reinforcement learning (MARL) framework that dynamically integrates EMV routing and adaptive traffic signal pre-emption. EMVLight leverages real-time traffic data and decentralized coordination between agents to optimize EMV travel paths while reducing disruptions to non-EMV traffic. Experimental results demonstrate a 42.6% reduction in EMV travel times and a 23.5% decrease in the average travel time for non-EMVs across synthetic and real-world traffic networks. Second, the dissertation develops the Dynamic Queue-Jump Lane (DQJL) system, which employs Multi-Agent Proximal Policy Optimization (MAPPO) to enable coordinated lane-clearing maneuvers in mixed-traffic environments comprising autonomous and human-driven vehicles. By dynamically forming queue-jump lanes in response to real-time traffic conditions, DQJL minimizes lane-change maneuvers and reduces EMV travel times by up

关键词： Travel time

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Application of text mining techniques to identify actual wrong-way driving (WWD) crashes in police reports

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International Journal of transportation Science and Technology 2023年第4期12卷 1038-1051页

作者： Parisa Hosseini Seyedalireza Khoshsirat Mohammad Jalayer Subasish Das Huaguo Zhou Department of Civil and Environmental Engineering Center for Research and Education in Advanced Transportation Engineering Systems(CREATEs)Rowan University201 Mullica Hill RoadGlassboroNJ 08028USA Department of Computer&Information Sciences University of Delaware18 Amstel AveNewarkDE 19716USA Civil Engineering Program Ingram School of EngineeringTexas State UniversityRFM 5202601 University DriveSan MarcosTX 78666USA Harbert Center Department of Civil and Environmental EngineeringAuburn UniversityAuburnAL 36849USA

Wrong-way driving(WWD)has been a long-lasting issue for transportation agencies and law enforcement,since it causes pivotal threats to road *** being rare,crashes occurring due to WWD are more severe than other types of *** order to analyze WWD crashes,there is a need to obtain WWD incidents or crash ***,it is time-consuming to identify actual WWD crashes from potential WWD crashes in large crash *** often involves large man-hours to review hardcopy of crash narratives in the police ***,it may cause an overestimation or underestimation of WWD crash *** fill this gap,the present study,as the first-of-its-kind,aims at identifying actual WWD crashes from potential WWD crashes in police reports by using machine learning ***,Bidirectional Encoder Representations from Transformers(BERT)models have shown promising results in natural language *** this study,we implemented the BERT model as well as five conventional classification algorithms,including Naïve Bayes(NB),Support Vector Machine(SVM),Decision Tree(DT),Random Forest(RF),and Single Layer Perceptron(SLP)to classify crash report narratives as actual WWD and non-WWD ***-validation and different performance metrics were used to evaluate the performance of each classification *** indicated that the BERT model outperformed in identifying actual WWD crashes in comparison with other algorithms with an accuracy of 81.59%.The BERT classification algorithm can be implemented to reduce the time needed to identify actual WWD crashes from crash report narratives.

关键词： Wrong-Way Driving Crashes Crash Report Narratives Text Mining Text Classification

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3D bioprinting of engineered exosomes secreted from M2-polarized macrophages through immunomodulatory biomaterial promotes in vivo wound healing and angiogenesis

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Bioactive Materials 2025年第3期45卷 345-362页

作者： Sayan Deb Dutta Jeong Man An Jin Hexiu Aayushi Randhawa Keya Ganguly Tejal V.Patil Thavasyappan Thambi Jangho Kim Yong-kyu Lee Ki-Taek Lim Department of Biosystems Engineering Kangwon National University24341ChuncheonRepublic of Korea Institute of Forest Science Kangwon National University24341ChuncheonRepublic of Korea School of Medicine University of California Davis95817SacramentoUnited States Department of Bioengineering College of EngineeringHanyang University04763SeoulRepublic of Korea Department of Plastic and Traumatic Surgery Capital Medical University100069BeijingChina Interdisciplinary Program in Smart Agriculture Kangwon National University24341ChuncheonRepublic of Korea Graduate School of Biotechnology College of Life SciencesKyung Hee University17104YonginRepublic of Korea Department of Convergence Biosystems Engineering Chonnam National University61186GwangjuRepublic of Korea Department of Chemical and Biological Engineering Korea National University of Transportation27470ChungjuRepublic of Korea

Biomaterial composition and surface charge play a critical role in macrophage polarization,providing a mo-lecular cue for immunomodulation and tissue *** this study,we developed bifunctional hydrogel inks for accelerating M2 macrophage polarization and exosome(Exo)cultivation for wound healing *** this,we first fabricated polyamine-modified three-dimensional(3D)printable hydrogels consisting of algi-nate/gelatin/polydopamine nanospheres(AG/NSPs)to boost M2-exosome(M2-Exo)*** cultivated M2-Exo were finally encapsulated into a biocompatible collagen/decellularized extracellular matrix(COL@d-ECM)bioink for studying angiogenesis and in vivo wound healing *** findings show that 3D-printed AGP hydrogel promoted M2 macrophage polarization by Janus kinase/signal transducer of activation(JAK/STAT),peroxisome proliferator-activated receptor(PPAR)signaling pathways and facilitated the M2-Exo ***,the COL@d-ECM/M2-Exo was found to be biocompatible with skin ***(RNA-Seq)and real-time PCR(qRT-PCR)study revealed that co-culture of fibroblast/keratinocyte/stem cells/endothelial cells in a 3D bioprinted COL@d-ECM/M2-Exo hydrogel upregulated the skin-associated signature biomarkers through various regulatory pathways during epidermis remodeling and downregulated the mitogen-activated protein kinase(MAPK)signaling pathway after 7 *** a subcutaneous wound model,the 3D bioprinted COL@d-ECM/M2-Exo hydrogel displayed robust wound remodeling and hair follicle(HF)induction while reducing canonical pro-inflammatory activation after 14 days,presenting a viable therapeutic strategy for skin-related disorders.

关键词： Exosome Immunomodulation Decellularized extracellular matrix Skin bioprinting Angiogenesis Wound healing

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Study on Green transportation Behaviors in Beijing, Tokyo, and Klang Valley based on Theory of Planned Behavior

Study on Green Transportation Behaviors in Beijing, Tokyo, a...

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IEEE International Conference on Intelligent transportation engineering (ICITE)

作者： Youfu Zheng Yida Chai Yiping Le Huapu Lu Innovative Global Program School of Engineering Shibaura Institute of Technology Tokyo Japan Institute of Transportation Research Tsinghua University Beijing China

ISBN: (数字)9798350314212

ISBN: (纸本)9798350314229

This study employed Structural Equation Modeling (SEM) to examine green travel behavior in three different urban areas: Beijing, Tokyo, and the Klang Valley. The analysis was based on the Theory of Planned Behavior (TPB), and it aimed to assess how three key variables (attitude, subjective norms, and perceived behavioral control) influenced green travel intentions, leading to the formulation of six hypotheses. The research findings revealed that the model was well-suited for Beijing and the Klang Valley, as indicated by several tests for structural fit. However, it was found to be less appropriate for Tokyo. In Beijing, the results suggested a strong intention among citizens to engage in green travel, implying a high willingness to use eco-friendly transportation options. In both Beijing and the Klang Valley, the three variables positively impacted green travel intentions, thus supporting the hypotheses derived from the TPB. However, the model was not as suitable for Tokyo. Moreover, the study highlighted that personal values and social pressures had limited influence across all three cities. Beijing and Klang Valley residents seemed to prioritize the ease of using green travel options, while Tokyo residents appeared to be less influenced by this factor. This could be attributed to Tokyo's highly developed rail transit system, where residents may choose green travel not solely due to their willingness but rather because of the convenience it offers.

关键词： Rails Analytical models Numerical analysis Attitude control Urban areas Mathematical models Green transportation

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A stochastic MPEC approach for grid tariff design with demand-side flexibility

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ENERGY SYSTEMS-OPTIMIZATION MODELING SIMULATION AND ECONOMIC ASPECTS 2023年第3期14卷 707-729页

作者： Askeland, Magnus Burandt, Thorsten Gabriel, Steven A. Energy Systems SINTEF Energy Research Trondheim Norway Department of Electric Power Engineering Norwegian University of Science and Technology Trondheim Norway Workgroup for Infrastructure Policy Technical University of Berlin Berlin Germany The Energy Transportation Environment Department German Institute for Economic Research (DIW Berlin) Berlin Germany Department of Industrial Economics and Technology Management Norwegian University of Science and Technology Trondheim Norway Department of Mechanical Engineering Applied Math and Statistics Scientific Computation Program University of Maryland College Park MD USA

As the end-users increasingly can provide flexibility to the power system, it is important to consider how this flexibility can be activated as a resource for the grid. Electricity network tariffs is one option that can be used to activate this flexibility. Therefore, by designing efficient grid tariffs, it might be possible to reduce the total costs in the power system by incentivizing a change in consumption patterns. This paper provides a methodology for optimal grid tariff design under decentralized decision-making and uncertainty in demand, power prices, and renewable generation. A bilevel model is formulated to adequately describe the interaction between the end-users and a distribution system operator. In addition, a centralized decision-making model is provided for benchmarking purposes. The bilevel model is reformulated as a mixed-integer linear problem solvable by branch-and-cut techniques. Results based on both deterministic and stochastic settings are presented and discussed. The findings suggest how electricity grid tariffs should be designed to provide an efficient price signal for reducing aggregate network peaks.

关键词： Bilevel problem Grid tariffs Mathematical program with equilibrium constraints (MPEC) Uncertainty

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