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Assessing the risk of pedestrian crossing behavior on suburban roads using structural equation model

Journal of Traffic and Transportation engineering(English Edition) 2024年第5期11卷 853-866页

作者： Shahrbanoo Kavianpour Farshidreza Haghighi Abbas Sheykhfard Subasish Das Grigorios Fountas Mohammad Mehdi Oshanreh Faculty of Civil Engineering Babol Noshirvani University of TechnologyBabolIran Ingram School of Engineering Texas State UniversitySan MarcosTX 78666USA School of Rural and Surveying Engineering Aristotle University of ThessalonikiThessaloniki 54124Greece Department of Civil and Environmental Engineering University of WashingtonSeattleWA 98195USA

While pedestrian crashes on suburban roads have received more attention over recent years,the role of pedestrian crossing risk in areas adjacent to pedestrian crossing facilities,such as pedestrian overpasses,has been *** pedestrians in suburban areas tend to avoid pedestrian overpasses,exhibiting crossing behaviors that increase the likelihood of pedestrian-involving *** a result of the presence of overpasses,drivers may think that there are no pedestrians in the surroundings,so they choose a speed based only on the prevailing traffic and road environment without accounting for potential interactions with ***,crashes will occur,with pedestrians typically being the most seriously affected *** this study,using video recordings from a suburban road in Amol-Babol,Iran,the risk of pedestrian crossing behavior in areas near pedestrian overpasses is *** speed selection behavior of drivers in these areas has also been examined using speedometer *** quantify the level of risk for pedestrians when interacting with approaching vehicles during the crossing movements,the post encroachment time(PET)was used as a surrogate safety *** on critical thresholds of PET,three different risk levels were identified using a K-means algorithm:high,medium,and low *** identify the elements affecting the risk of pedestrian crossing behavior,structural equation models were estimated for all three risk *** results showed that human factors,relating to both drivers and pedestrians,have a dominant impact on pedestrian safety,especially in high and medium risk *** and vehicle factors were also found to have statistically observable effects on pedestrian safety,but to a milder extent compared to human *** findings of this study highlight the need for intervening in several aspects of vehicle-pedestrian interactions with critical importance for pedestrian safety,including road users’performance

关键词： Pedestrian Crossing safety Suburban roads Speed Structural equation model

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Exploring the mechanical properties of steel-and polypropylene-reinforced ultra-high-performance concrete through numerical analyses and experimental multi-target digital image correlation

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Frontiers of Structural and civil engineering 2023年第8期17卷 1228-1248页

作者： Behrooz DADMAND Hamed SADAGHIAN Sahand KHALILZADEHTABRIZI Masoud POURBABA Amir MIRMIRAN Department of Civil Engineering University of RaziKermanshah ***Iran Department of Civil Engineering University of TabrizTabriz ***Iran Ingram School of Engineering Texas State UniversitySan MarcosTX 78666USA University of Texas at Tyler TylerTX 75799USA

This study presents experimental and numerical investigations on the mechanical properties of ultrahigh-performance concrete(UHPC)reinforced with single and hybrid micro-and macro-steel and polypropylene *** this purpose,a series of cubic,cylindrical,dog-bone,and prismatic beam specimens(total fiber by volume=1%,and 2%)were tested under compressive,tensile,and flexural loadings.A method,namely multi-target digital image correlation(MT-DIC)was used to monitor the displacement and deflection *** obtained experimental data were subsequently used to discuss influential parameters,i.e.,flexural strength,tensile strength,size effect,*** analyses were also carried out using finite element software to account for the sensitivity of different ***,nonlinear regression analyses were conducted to obtain the flexural load-deflection *** results showed that the MT-DIC method was capable of estimating the tensile and flexural responses as well as the location of the crack with high *** addition,the regression analyses showed excellent consistency with the experimental results,with correlation coefficients close to ***,size-effect modeling revealed that modified Bazant theory yielded the best estimation of the size-effect phenomenon compared to other models.

关键词： UHPC MT-DIC flexural behavior tensile behavior steel fiber polypropylene fiber

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Optimization of machine learning models for predicting the compressive strength of fiber-reinforced self-compacting concrete

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Frontiers of Structural and civil engineering 2023年第2期17卷 284-305页

作者： Hai-Van Thi MAI May Huu NGUYEN Son Hoang TRINH Hai-Bang LY Civil Engineering Department University of Transport TechnologyHanoi 100000Vietnam Civil and Environmental Engineering Program Graduate School of Advanced Science and EngineeringHiroshima UniversityHiroshima 739-8527Japan

Fiber-reinforced self-compacting concrete(FRSCC)is a typical construction material,and its compressive strength(CS)is a critical mechanical property that must be adequately *** the machine learning(ML)approach to estimating the CS of FRSCC,the current research gaps include the limitations of samples in databases,the applicability constraints of models owing to limited mixture components,and the possibility of applying recently proposed *** study developed different ML models for predicting the CS of FRSCC to address these *** neural network,random forest,and categorical gradient boosting(CatBoost)models were optimized to derive the best predictive model with the aid of a 10-fold cross-validation technique.A database of 381 samples was created,representing the most significant FRSCC dataset compared with previous studies,and it was used for model *** findings indicated that CatBoost outperformed the other two models with excellent predictive abilities(root mean square error of 2.639 MPa,mean absolute error of 1.669 MPa,and coefficient of determination of 0.986 for the test dataset).Finally,a sensitivity analysis using a partial dependence plot was conducted to obtain a thorough understanding of the effect of each input variable on the predicted CS of *** results showed that the cement content,testing age,and superplasticizer content are the most critical factors affecting the CS.

关键词： compressive strength self-compacting concrete artificial neural network decision tree CatBoost

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Prediction of the mechanical performance of polyethylene fiber-based engineered cementitious composite(PE-ECC)

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Low-carbon Materials and Green Construction 2024年第1期2卷 354-378页

作者： Shameem Hossain Md Nasir Uddin Kangtai Yan Md Minaz Hossain Md Sabbir Hossen Golder Md Ahatasamul Hoque Department of Bridge Engineering College of Civil EngineeringTongji UniversityShanghaiChina Department of Disaster Mitigation for Structures College of Civil EngineeringTongji UniversityShanghaiChina Ingram School of Engineering Texas State UniversitySan MarcosTX 78666USA

In recent years,extensive research has focused on applying machine learning(ML)techniques to predict the properties of engineered cementitious composites(ECCs).ECCs exhibit crucial characteristics such as compressive strength(CS),tensile strength(TS),and tensile strain(TSt).Accurate forecasting of these critical properties can reduce material waste,lower construction expenses,and expedite project timelines for engineers and *** study investigates mixture design components and corresponding strengths of ECCs based on only polyethylene fiber drawing from existing *** neural network(ANN)models are developed to predict CS,TS,and TSt using a dataset of 339 experimental results with twelve input *** ANN models,implemented in MATLAB,consider various hidden layers and neurons to optimize accuracy and validation metrics demonstrate the model’s high *** analysis explores individual parameter *** inspiration from this study,it would be advantageous to enhance the predictive modeling toolkit by leveraging the progress made in existing technologies,thereby driving the green and low-carbon development of civil *** approach not only improves the efficiency and sustainability of construction practices but also aligns with global environmental goals by reducing the carbon footprint associated with civil engineering projects.

关键词： Engineered cementitious composites Polyethylene fibers Artificial neural network Compressive strength Tensile strength Tensile strain

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Experimental Performance Evaluation of Geotechnical Encased Columns

Experimental Performance Evaluation of Geotechnical Encased ...

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Geotechnical Frontiers 2025: Soil Characterization and Improvement

作者： Gonzalez, Etienne Almikati, Abdurrahman Zornberg, Jorge Gabriel Dept. of Civil Architectural and Environmental Engineering Univ. of Texas at Austin United States Ingram School of Engineering Texas State Univ. United States

ISBN: (数字)9780784486016

This study investigated the performance of geosynthetic encased columns (GECs) in soft soils using physical models with a transparent clay surrogate. The objective was to identify optimal design conditions that maximize the stabilization provided by the encasement while minimizing risks associated with insufficient or excessive reinforcement. Two representative GEC models were selected to illustrate different performance outcomes: a baseline model with medium stiffness encasement and a model with high stiffness encasement. Detailed examination of these models highlighted the importance of tailoring encasement confinement to facilitate performance gains while avoiding over-design. Encasement tensile strength significantly reduced column bulging compared to unreinforced and low-stiffness models. Mobilized encasement confinement enabled constant incremental bearing capacity enhancements, provided tensile strains remained within allowable limits. High-stiffness encasement models achieved rapid confinement mobilization at lower strains due to the steep tensile strength-strain slope. Column infill deviatoric stress versus axial strain relationships captured performance and onset of column failure in detail. Results demonstrated encasement tensile capacity controls GEC failure in very soft soils, with bearing capacity depending primarily on encasement stiffness and strength rather than column infill properties. © ASCE.

关键词： Tensile strength

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Determining the Shear Bands between GECs and Soft Clays with the Aid of Digital Image Correlation

Determining the Shear Bands between GECs and Soft Clays with...

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Geotechnical Frontiers 2025: Foundations, Retaining Structures, and Geosynthetics

作者： Almikati, Abdurrahman Gonzalez, Etienne Zornberg, Jorge Gabriel Ingram School of Engineering Texas State Univ. United States Dept. of Civil Architectural and Environmental Engineering Univ. of Texas Austin United States

ISBN: (数字)9780784485972

Geosynthetic encased columns (GECs) are widely used to stabilize soft clays, mainly under embankments. This ground improvement technique increases the load-bearing capacity, reduces settlements, and enhances the stability of the system. The majority of the research studies in the literature focus on the overall degree of improvement obtained when using different area replacement ratios, different geosynthetic materials, varying infill materials, and other factors. However, there has been very limited research on the interaction between the GECs and the surrounding soft clays along the length of the GECs. It is crucial as it allows quantifying the shear stresses and shear band development in the zones around the GECs. To tackle this issue, an experimental setup has been developed to axially load GECs that are surrounded by Laponite, a transparent material behaving like clays. Using Laponite as a soft clay surrogate enables continuous visualization and quantification of GEC and Laponite strains during testing. This paper focuses on the methodology, testing setup, and analysis techniques used to evaluate the shear stresses and shear bands. The experimental setup consisted of a transparent cell chamber with a diameter of 203.2 mm and a height of 609.6 mm. One test was performed by constructing one central GEC with a diameter of 80 mm, with varying geosynthetic materials. Through collecting continuous photos of the testing setup while loading, it was possible to detect strains through advanced digital image correlation (DIC) analysis. Results also included calculation of the interface shear throughout the length of the sample. This methodology provides promising results on the interaction of geosynthetic encased columns and surrounding soils under simulated field conditions. © ASCE.

关键词： Shear bands

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Low-cost real-time environmental noise monitoring system design and implementation

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International Journal of Environmental Studies 2024年第3期81卷 1045-1057页

作者： Hamamcı, Samet Feyyaz Doğru, Ahmet Özgür Graduate School Geographical Information Technologies PhD Program Istanbul Technical University Istanbul Turkey Faculty of Civil Engineering Department of Geomatics Engineering Istanbul Technical University Istanbul Turkey

Environmental noise has a negative impact on human health and well-being. Real-time noise monitoring for effective noise control and management helps to identify noise sources and areas with high noise pollution, and potential mitigation strategies in urban planning. This paper reports work with a pilot scale system that can continuously collect noise data to assess noise levels accurately for real-time monitoring of environmental noise, focusing on hardware and software aspects and potential applications. The system permits the transfer of measurement data to the central server computer, with storage and display there, with the code written using Python. This system includes Web integration, with open source technologies to provide data access, and is based on Wi-Fi communication and a Control Card with Wi-Fi technology. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Noise pollution

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Application of machine learning technique for predicting and evaluating chloride ingress in concrete

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Frontiers of Structural and civil engineering 2022年第9期16卷 1153-1169页

作者： Van Quan TRAN Van Loi GIAP Dinh Phien VU Riya Catherine GEORGE Lanh Si HO Department of Civil Engineering University of Transport TechnologyHanoi 100000Vietnam Civil and Environmental Engineering Program Graduate School of Advanced Science and EngineeringHiroshima UniversityHiroshima 739-8527Japan

The degradation of concrete structure in the marine environment is often related to chloride-induced corrosion of reinforcement ***,the chloride concentration in concrete is a vital parameter for estimating the corrosion level of reinforcement *** research aims at predicting the chloride content in concrete using three hybrid models of gradient boosting(GB),artificial neural network(ANN),and random forest(RF)in combination with particle swarm optimization(PSO).The input variables for modeling include exposure condition,water/binder ratio(W/B),cement content,silica fume,time exposure,and depth of *** results indicate that three models performed well with high accuracy of prediction(R2⩾0.90).Among three hybrid models,the model using GB_PSO achieved the highest prediction accuracy(R2=0.9551,RMSE=0.0327,and MAE=0.0181).Based on the results of sensitivity analysis using SHapley Additive exPlanation(SHAP)and partial dependence plots 1D(PDP-1D),it was found that the exposure condition and depth of measurement were the two most vital variables affecting the prediction of chloride *** the number of different exposure conditions is larger than two,the exposure significantly impacted the chloride content of concrete because the chloride ion ingress is affected by both chemical and physical *** study provides an insight into the evaluation and prediction of the chloride content of concrete in the marine environment.

关键词： gradient boosting random forest chloride content concrete sensitivity analysis

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A hybrid machine learning model to estimate self-compacting concrete compressive strength

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Frontiers of Structural and civil engineering 2022年第8期16卷 990-1002页

作者： Hai-Bang LY Thuy-Anh NGUYEN Binh Thai PHAM May Huu NGUYEN Civil Engineering Department University of Transport TechnologyHanoi 100000Vietnam Civil and Environmental Engineering Program Graduate School of Advanced Science and EngineeringHiroshima UniversityHiroshima 739-8527Japan

This study examined the feasibility of using the grey wolf optimizer(GWO)and artificial neural network(ANN)to predict the compressive strength(CS)of self-compacting concrete(SCC).The ANN-GWO model was created using 115 samples from different sources,taking into account nine key SCC *** validation of the proposed model was evaluated via six indices,including correlation coefficient(R),mean squared error,mean absolute error(MAE),IA,Slope,and mean absolute percentage *** addition,the importance of the parameters affecting the CS of SCC was investigated utilizing partial dependence *** results proved that the proposed ANN-GWO algorithm is a reliable predictor for SCC’s *** that,an examination of the parameters impacting the CS of SCC was provided.

关键词： artificial neural network grey wolf optimize algorithm compressive strength self-compacting concrete

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Effect of Prestressing Level on a Lead Damper with Straight Shaft: An Experimental Investigation 18th

Effect of Prestressing Level on a Lead Damper with Straight ...

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18th World Conference on Seismic Isolation, Energy Dissipation, and Active Vibration Control of Structures, WCSI 2023

作者： Soydan, Cihan Çalım, Furkan Güllü, Ahmet Yüksel, Ercan Department of Civil Engineering Tekirdag Namik Kemal University Tekirdag Turkey Faculty of Civil Engineering Istanbul Technical University Istanbul Turkey Ingram School of Engineering Texas State University San MarcosTX78666 United States

ISBN: (纸本)9783031710476

The prestressed lead damper is an energy-dissipative device with a high damping capacity and is used for seismic response control in the earthquake-resistant design of structures. The device works as a friction damper and utilizes the friction losses between lead and a moving shaft while prestressing on the lead controls the behavior. This study experimentally investigates the effect of prestressing level on the characteristics of a lead damper. After producing the lead damper with a straight shaft, different prestressing levels were applied to the lead, i.e., 0.0%, 0.5%, 1.0%, 1.5%, 2.0%, 3.0% and 4.0%. The dampers were experimentally tested by applying a three-step displacement protocol to the shaft: five cycles of 5.0 mm target displacement with a rate of 0.5 mm/s, five cycles of 10.0 mm target displacement with a rate of 1.0 mm/s, and ten cycles of 20.0 mm target displacement with a rate of 2.0 mm/s. The cyclic tests showed that the lead damper’s characteristics, e.g., damper force, initial stiffness, hysteretic behavior, and damping properties, were considerably affected by the amount of prestressing on lead. Increasing the lead prestressing levels resulted in more stable hysteretic curves. In addition, the damper force and, consequently, the damping properties were significantly improved. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Damping

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