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Prediction of mechanical properties of eco-friendly concrete using machine learning algorithms and partial dependence plot analysis

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Smart Construction and Sustainable Cities 2025年第1期3卷 1-21页

作者： Roy, Tonmoy Das, Pobithra Jagirdar, Ravi Shhabat, Mousa Abdullah, Md Shahriar Kashem, Abul Rahman, Raiyan Data Analytics & Information Systems Utah State University Logan USA Department of Civil Engineering Leading University Sylhet Bangladesh Traffic Engineer Fastech Consulting Engineers New Jersey USA Faculty of Engineering Department of Civil Engineering The University of Jordan Amman Jordan Department of Civil and Environmental Engineering Lamar University Beaumont USA Department of Information Science and Library Management University of Rajshahi Rajshahi Bangladesh

Rice husk ash concrete (RHAC) shows promise as a beneficial supplementary material in concrete. However, determining mechanical properties such as compressive strength (CS) and splitting tensile strength (STS) of RHAC through conventional lab-scale methods is laborious and time-consuming. In this research, seven important variables were selected as inputs to predict CS and STS using machine learning (ML) models, including Gaussian Process Regression (GPR), Random Forest Regression (RFR), and Decision Tree Regression (DTR) with grid search optimization. The result presented revealed that selected machine learning models provide well accuracy for CS and STS estimates. Among these, the DTR model demonstrated superior performance, with CS prediction R2, RMSE, MAE, and MAPE values of 0.964, 3.314, 2.225, and 5.068, at the testing stage respectively. For STS at the testing stage, DTR achieved R2 of 0.969, RMSE of 0.177, MAE of 0.1322, and MAPE of 3.413. GPR and RFR models also performed well, with R2 values of 0.9434 and 0.9530 for CS prediction. The partial dependence plot (PDP) analysis revealed the optimal mix design parameters for achieving the desired strength. These results offer valuable insights for sustainable construction, allowing engineers to efficiently predict and optimize material performance, reducing the reliance on time-consuming lab methods.

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Risk-Reward Allocation among Integrated Project Delivery Method Stakeholders: A Gamified Cooperative Data Simulation Approach

Risk-Reward Allocation among Integrated Project Delivery Met...

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ASCE International Conference on Computing in civil engineering 2023: Visualization, Information Modeling, and Simulation, i3CE 2023

作者： Eissa, Radwa Abdul Nabi, Mohamad El-Adaway, Islam H. Dept. of Civil Architectural and Environmental Engineering Missouri Univ. of Science and Technology RollaMO United States Hurst-McCarthy Professor of Construction Engineering and Management Prof. of Civ. Eng. and Founding Director of Missouri Consortium of Construction Innovation Dept. of Civ. Arch. and Environ. Eng. and Dept. of Engineering Management and Systems Engineering Missouri Univ. of Science and Technology RollaMO United States

ISBN: (数字)9780784485231

ISBN: (纸本)9780784485231

Integrated project delivery (IPD) method is associated with numerous benefits, including enhanced project performance, collaboration, and information sharing among project stakeholders. However, the lack of adequate incentive and reward mechanisms is still considered as the main reason for slowing down the adoption of IPD. As such, the goal of this paper is to identify a fair and efficient risk pool distribution among IPD project stakeholders. The adopted methodology included (1) assigning the risks associated with each stakeholder, (2) computing valuations for all possible subset coalitions among IPD project stakeholders that reflect their risk control capabilities as well as their coordination effects, and (3) utilizing cooperative game theory to generate fair and efficient distribution mechanisms given different a priori working and coalitional preferences. As such, a total of 22 risks were identified and quantified to calculate the risk control valuations of all subset coalitions using Monte-Carlo simulations. Afterward, these valuations were utilized to generate fair risk and reward distributions using the Shapley value and Owen value for games with a priori unions. The coalitional stability of the generated results was evaluated using propensity-to-disrupt ratios. Ultimately, the findings of this paper indicate that an IPD-multiparty agreement where all parties are engaged early on in establishing the contract results in the most balanced willingness to cooperate among stakeholders. Finally, the outcome of this paper equips industry practitioners with a distribution mechanism that corresponds to the adopted IPD relational configuration on one hand and the overall IPD project stability on the other. © 2024 Computing in civil engineering 2023: Visualization, Information Modeling, and Simulation - Selected Papers from the ASCE International Conference on Computing in civil engineering 2023. All rights reserved.

关键词： Game theory

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Integrating Real-Time Machine Learning and Dynamic Charging for Electric Vehicle Routing

Integrating Real-Time Machine Learning and Dynamic Charging ...

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Annual Conference on Information Sciences and systems (CISS)

作者： Mandana Farhang Ghahfarokhi Hyoshin Park Venktesh Pandey Gyugeun Yoon Department of Engineering Management and Systems Engineering Old Dominion University Norfolk VA USA Department of Civil Architectural and Environmental Engineering North Carolina A&T State University Greensboro NC USA Department of Civil Engineering Seoul National University Seoul South Korea

ISBN: (数字)9798331513269

ISBN: (纸本)9798331513276

As electric vehicles (EVs) gain popularity, the demand for efficient EV routing and innovative charging solutions is increasing. This study proposes a novel integration of Temporal Multimodal Multivariate Learning (TMML) and the Time-Dependent Shortest Path (TDSP) algorithm to dynamically optimize EV routing while incorporating stationary and wireless charging systems. TMML continuously updates travel time distributions based on real-time observations, reducing uncertainties and enabling more accurate predictions. These updated distributions are utilized by TDSP to determine the most efficient routes, considering time-dependent travel times, and the availability of charging infrastructure. The methodology integrates Mobile Energy Distributors (MEDs) and Dynamic Inductive Charging (DIC). MED dynamically dispatches portable charging units, while DIC enables on-the-move charging through road-embedded systems. Real-world data from the Washington, DC metropolitan area, sourced from Regional Integrated Transportation Information System (***), provides granular insights into traffic patterns and charging infrastructure performance. This hybrid system operates by extracting feasible routes, evaluating them using TMML and TDSP, and selecting the optimal path to minimize travel time, waiting time, and charging costs.

关键词： Uncertainty Heuristic algorithms Inductive charging Urban areas Transportation Reinforcement learning Traffic control Routing Real-time systems Vehicle dynamics

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Distributed Cooperative Localization with Efficient Pairwise Range Measurements 1

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15th International Symposium on Distributed Autonomous Robotic systems, DARS 2021 and 4th International Symposium on Swarm Behavior and Bio-Inspired Robotics, SWARM 2021

作者： Quraishi, Anwar Martinoli, Alcherio Distributed Intelligent Systems and Algorithms Laboratory School of Architecture Civil and Environmental Engineering École Polytechnique Fédérale de Lausanne Lausanne Switzerland

ISBN: (数字)9783030927905

ISBN: (纸本)9783030927899

We present a method based on covariance intersection for cooperative localization with pairwise range-only relative measurements. Our method was designed for underwater robots equipped with an acoustic communication and ranging system. Range measurements are not sufficient to compute a complete relative 3D position. Therefore, covariance intersection is performed in a transformed space along their relative estimated positions, while preserving cross-correlations between other state variables. Given the characteristics of the acoustic channel, only one robot can transmit data or a ranging request at a time, hence the pairwise limitation. We also present a heuristic for choosing a peer robot for a range measurement by maximizing mutual information. Our method places no further restrictions on the order, timing or scheduling of relative measurements. We evaluated our method for accuracy and consistency, and present results from simulations as well as outdoor experiments. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Robots

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Optimal Deployment of an Equitable Cav Platoonable Corridor On Road Networks with Mixed Traffic Flow

SSRN

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SSRN 2023年

作者： Zhu, Dan Xie, Tingting Liu, Yang Zou, Bo Rujeerapaiboon, Napat Department of Industrial Systems Engineering and Management National University of Singapore Singapore Department of Civil and Environmental Engineering National University of Singapore Singapore Department of Civil Materials and Environmental Engineering University of Illinois Chicago United States

Connected and autonomous vehicle (CAV) technology is expected to increase road capacity and reduce fuel consumption, but it may take time for all human-driven vehicles (HVs) to be replaced by CAVs. During the transition period, CAVs and HVs will continue to coexist. This paper aims to find an optimal deployment of a CAV platoonable corridor to facilitate CAV platooning and better manage traffic congestion on road networks with a mixed traffic flow of CAVs and HVs. Generally, dedicated infrastructure for CAVs, such as CAV lanes, zones, or corridors, may have distributional welfare effects on travelers. Especially, HV users may be worse off because road space is partially converted to dedicated infrastructure for CAVs. To facilitate equitable infrastructure planning, we propose a bi-level Stackelberg game that incorporates the equity concern, where the planner acts as the leader to determine the optimal deployment of an equitable CAV platoonable corridor at the upper level, while travelers are followers who make user-optimal route choices given the CAV platoonable corridor at the lower level. For the lower-level problem, we model the decision-making of travelers as a platoon-embedded network equilibrium with mixed flow (PNEMF) and derive its equivalent variational inequality (VI) problem. The existence and the uniqueness of the VI solution are proved. To solve the bi-level program efficiently, we propose a simulated-annealing-based corridor search (SACS) algorithm. Our numerical experiments on the Nguyen-Dupuis(ND) and Sioux Falls (SF) networks demonstrate the expected benefits of deploying a CAV platoonable corridor if the CAV market penetration ratio exceeds a certain threshold. In the meantime, both CAV and HV users are better off with the deployed equitable corridor. In addition, we find that the degree of the transportation planner's concern about the inequity issue has a significant impact on the corridor design when the CAV penetration ratio is low. With less c

关键词： Autonomous vehicles

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Evaluation of Connected Vehicle Identification-Aware Mixed Traffic Freeway Cooperative Merging

Evaluation of Connected Vehicle Identification-Aware Mixed T...

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IEEE Symposium on Intelligent Vehicle

作者： Haoji Liu Fatemeh Jahedinia Zeyu Mu B. Brian Park Link Lab and the Department of Systems and Information Engineering University of Virginia Charlottesville VA USA Department of Civil and Environmental Engineering University of Virginia Charlottesville VA USA

ISBN: (数字)9798350348811

ISBN: (纸本)9798350348828

Cooperative on-ramp merging control for connected automated vehicles (CAVs) has been extensively investigated. However, they did neglect the connected vehicle identification process, which is a must for CAV cooperations. In this paper, we introduced a connected vehicle identification system (VIS) into the on-ramp merging control process for the first time and proposed an evaluation framework to assess the impacts of VIS on on-ramp merging performance. First, the mixed-traffic cooperative merging problem was formulated. Then, a real-world merging trajectory dataset was processed to generate dangerous merging scenarios. Aiming at resolving the potential collision risks in mixed traffic where CAVs and traditional human-driven vehicles (THVs) coexist, we proposed on-ramp merging strategies for CAVs in different mixed traffic situations considering the connected vehicle identification process. The performances were evaluated via simulations. Results indicated that while safety was assured for all cases with CAVs, the cases with VIS had delayed initiation of cooperation, limiting the range of cooperative merging and leading to increased fuel consumption and acceleration variations.

关键词： Connected vehicles Simulation Merging Traffic control Energy efficiency Safety Trajectory

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Human-Machine Interaction in Automated Vehicles: Reducing Voluntary Driver Intervention

arXiv

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arXiv 2024年

作者： Zhong, Xinzhi Zhou, Yang Kamaraj, Varshini Zhou, Zhenhao Kontar, Wissam Negrut, Dan Lee, John D. Ahn, Soyoung Department of Civil and Environmental Engineering University of Wisconsin-Madison United States Zachry Department of Civil and Environmental Engineering Texas A&M College Station United States Department of Industrial and Systems Engineering University of Wisconsin-Madison United States Department of Mechanical Engineering University of Wisconsin-Madison United States

This paper develops a novel car-following control method to reduce voluntary driver interventions and improve traffic stability in Automated Vehicles (AVs). Through a combination of experimental and empirical analysis, we show how voluntary driver interventions can instigate substantial traffic disturbances that are amplified along the traffic upstream. Motivated by these findings, we present a framework for driver intervention based on evidence accumulation (EA), which describes the evolution of the driver’s distrust in automation, ultimately resulting in intervention. Informed through the EA framework, we propose a deep reinforcement learning (DRL)-based car-following control for AVs that is strategically designed to mitigate unnecessary driver intervention and improve traffic stability. Numerical experiments are conducted to demonstrate the effectiveness of the proposed control model. The code supporting the findings of this study are available in Github page. © 2024, CC BY.

关键词： Reinforcement learning

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Real-Time Activity Duration Extraction of Crane Works for Data-Driven Discrete Event Simulation 22

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

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Proceedings of the Winter Simulation 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

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.

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To what extent can solids retention time be reduced without deteriorating the performance of the thermal hydrolysis pretreatment-enhanced mesophilic anaerobic digestion 96

To what extent can solids retention time be reduced without ...

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96th Annual Water Environment Federation Technical Exhibition and Conference, WEFTEC 2023

作者： Li, Yitao Luo, Hao Strawn, Mary Racey, Lisa Haile, Fasil Balchunas, Brian Moline, Christopher Hentz, Lawrence Wang, Zhi-Wu Department of Civil and Environmental Engineering Virginia Tech BlacksburgVA24061 United States Arlington County Water Pollution Control Bureau ArlingtonVA22202 United States HDR Engineering Inc. ViennaVA22180 United States Department of Biological Systems Engineering Virginia Tech BlacksburgVA24061 United States

The solids retention time of anaerobic digesters is one of the most important parameters for operational and design purposes. It has been believed to have the potential for process intensification with thermal hydrolysis pretreatment upstream. To assess the extent and impact of digester solids retention time reduction, the digestibility and dewaterability of three pilot-scale, continuous flow, mesophilic anaerobic digesters operating at 15-, 12.5-, and 10-day SRT were evaluated. These 10-liter digesters were fed with 9% total solids CAMBITM THP-processed primary and wasted activated sludge every day for nearly a year. The study revealed that shortening the solids retention time from 15 to 10 days linearly decreased volatile solids reduction from 60.7% to 53.6%, highlighting the reduction in hydrolysis rate as the major drawback. However, other digester performance parameters such as methane yield, rheology, and dewaterability largely remained unchanged. Copyright © 2023 Water Environment Federation.

关键词： Anaerobic digestion

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Global increase in the occurrence and impact of multiyear droughts

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Journal of Bio-X Research 2025年第6731期387卷 278-284页

作者： Chen, Liangzhi Brun, Philipp Buri, Pascal Fatichi, Simone Gessler, Arthur McCarthy, Michael James Pellicciotti, Francesca Stocker, Benjamin Karger, Dirk Nikolaus Swiss Federal Institute for Forest Snow and Landscape Research WSL Birmensdorf Switzerland Department of Civil and Environmental Engineering National University of Singapore Singapore Department of Environmental Systems Science ETH Zurich Zurich Switzerland Institute of Science and Technology Austria (ISTA) Klosterneuburg Austria Institute of Geography University of Bern Bern Switzerland Oeschger Centre for Climate Change Research University of Bern Bern Switzerland

Persistent multiyear drought (MYD) events pose a growing threat to nature and humans in a changing climate. We identified and inventoried global MYDs by detecting spatiotemporally contiguous climatic anomalies, showing that MYDs have become drier, hotter, and led to increasingly diminished vegetation greenness. The global terrestrial land affected by MYDs has increased at a rate of 49,279 ± 14,771 square kilometers per year from 1980 to 2018. Temperate grasslands have exhibited the greatest declines in vegetation greenness during MYDs, whereas boreal and tropical forests have had comparably minor responses. With MYDs becoming more common, this global quantitative inventory of the occurrence, severity, trend, and impact of MYDs provides an important benchmark for facilitating more effective and collaborative preparedness toward mitigation of and adaptation to such extreme events. © 2025 American Association for the Advancement of Science. All rights reserved.

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