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Accurate accident prediction for highway-rail grade crossings (HRGCs) is critically important for assisting at-grade safety improvement decision making. Numerous machine-learning methods were developed focusing on predicting accidents and identifying contributing physical and operational characteristics. A more advanced deep learning-based model is explored as a more accurate means of predicting HRGC crashes compared to machine learning-based approaches. In particular, the prediction performance of the convolution neural network (CNN) model is compared to the most commonly used machine learning methods, such as decision tree (DT) and random forests (RF). A 19-year HRGCs data in North Dakota (ND) is used in this study. Training a machine learning model on an imbalanced data (e.g., unequal distribution of labelled data in accident and no-accident classes) introduce unique challenges for accurate prediction especially for minority class. In this paper, a resampling approach was used to address the imbalanced data issue. Various performance measurements are used to compare the models’ prediction performance. The results indicate that resampling the imbalanced dataset significantly improves the recall rate. The results also show that the proposed deep learning-based approach which deepens the layer levels and adapts to the training dataset has better prediction performance compared to other machine learning-based methods.
Lu Gao; Pan Lu; Yihao Ren. A Deep Learning Approach for Imbalanced Crash Data in Predicting Highway-Rail Grade Crossings Accidents. Reliability Engineering & System Safety 2021, 108019 .
AMA StyleLu Gao, Pan Lu, Yihao Ren. A Deep Learning Approach for Imbalanced Crash Data in Predicting Highway-Rail Grade Crossings Accidents. Reliability Engineering & System Safety. 2021; ():108019.
Chicago/Turabian StyleLu Gao; Pan Lu; Yihao Ren. 2021. "A Deep Learning Approach for Imbalanced Crash Data in Predicting Highway-Rail Grade Crossings Accidents." Reliability Engineering & System Safety , no. : 108019.
Introduction: Highway-rail at-grade crossings (HRGCs) are critical locations where a railway and a roadway intersect with one another. Crashes at those locations often result in fatalities and economic and social damages due to the impacts on both road and rail users. The main purpose of countermeasures at HRGCs is to permit safe and efficient rail and highway operations. Method: Countermeasures at highway-rail grade crossings (HRGCs) considered in this study include all traffic control devices and other warning and barrier devices at or on approaches to crossings. In general, active devices are commonly accepted as more effective countermeasures than passive devices. However, many of the previous effectiveness studies are either at the project level or were conducted without considering the before-improvement condition. This study focuses on the network-level marginal effectiveness of countermeasures on crash rate and severity levels during the 29-year study period from 1990 to 2018 by fully considering before-improvement control levels. A competing risk model (CRM) is able to accommodate the competing nature of crash severities as multiple outcomes from the same event of interest, which is crash occurrence in this study. Subsequently, CRM is used in this study as an integrated one-step estimation approach that investigates both crash frequency and severity likelihood over time. Results: The study findings indicate that adding audible devices to crossings already equipped with gates will result in a considerable annual decline in crash occurrence likelihood (0.25%). The same device installed at crossings already controlled by gates and flashing lights results in less reduction in crash occurrence likelihood of 0.14%. Moreover, adding a stop sign to the active crossing controls of gates, standard flashing lights, and audible devices will lead to a decrease in the probability of crash occurrence and severe crashes (injury and fatal). However, adding stop signs to crossings equipped only with crossbucks will increase the crash occurrence.
Amin Keramati; Pan Lu; Yihao Ren; Denver Tolliver; Chengbo Ai. Investigating the effectiveness of safety countermeasures at highway-rail at-grade crossings using a competing risk model. Journal of Safety Research 2021, 78, 251 -261.
AMA StyleAmin Keramati, Pan Lu, Yihao Ren, Denver Tolliver, Chengbo Ai. Investigating the effectiveness of safety countermeasures at highway-rail at-grade crossings using a competing risk model. Journal of Safety Research. 2021; 78 ():251-261.
Chicago/Turabian StyleAmin Keramati; Pan Lu; Yihao Ren; Denver Tolliver; Chengbo Ai. 2021. "Investigating the effectiveness of safety countermeasures at highway-rail at-grade crossings using a competing risk model." Journal of Safety Research 78, no. : 251-261.
Deteriorated sections decide the bearing capacity of beams. Because reinforced concrete beams are nonhomogeneous materials and often have variable sections along the beam, it is a challenge to precisely measure flexural rigidity of damage or repaired sections. This paper proposes a resonance-based non-destructive approach to identify section flexural rigidity for non-uniform simply supported beams. In the method, the resonant force is applied to the beam at the mid-span to measure the accelerations of evenly spaced segments and the strains of sections. The amplitude of the mid-span displacement is limited to approximately 1/104 of the span to avoid new damage. The proposed method can transform the calculation of dynamic bending moment and curvature into an equivalent static calculation to formulate a straightforward algorithm and easy-to-use equation on section flexural rigidity. It is applicable to non-uniform simply supported beams with arbitrary stiffness distribution. Theoretical and test results of a reinforced concrete and a non-uniform steel beam show that the identified section flexural rigidity would converge the exact result with the increased numbers of evenly spaced segments, and no less than five evenly spaced segments are sufficient in engineering applications.
Danguang Pan; Zhiyao Feng; Pan Lu; Zijian Zheng; Bincheng Zhao. Resonance-based approach for section flexural rigidity identification of simply supported beams. Engineering Structures 2021, 236, 112070 .
AMA StyleDanguang Pan, Zhiyao Feng, Pan Lu, Zijian Zheng, Bincheng Zhao. Resonance-based approach for section flexural rigidity identification of simply supported beams. Engineering Structures. 2021; 236 ():112070.
Chicago/Turabian StyleDanguang Pan; Zhiyao Feng; Pan Lu; Zijian Zheng; Bincheng Zhao. 2021. "Resonance-based approach for section flexural rigidity identification of simply supported beams." Engineering Structures 236, no. : 112070.
The platform of a microscopic traffic simulation provides an opportunity to study the driving behavior of vehicles on a roadway system. Compared to traditional conventional cars with human drivers, the car-following behaviors of autonomous vehicles (AVs) and connected autonomous vehicles (CAVs) would be quite different and hence require additional modeling efforts. This paper presents a thorough review of the literature on the car-following models used in prevalent micro-simulation tools for vehicles with both human and robot drivers. Specifically, the car-following logics such as the Wiedemann model and adaptive cruise control technology were reviewed based on the vehicle’s dynamic behavior and driving environments. In addition, some of the more recent “AV-ready (autonomous vehicles ready) tools” in micro-simulation platforms are also discussed in this paper.
Hafiz Ahmed; Ying Huang; Pan Lu. A Review of Car-Following Models and Modeling Tools for Human and Autonomous-Ready Driving Behaviors in Micro-Simulation. Smart Cities 2021, 4, 314 -335.
AMA StyleHafiz Ahmed, Ying Huang, Pan Lu. A Review of Car-Following Models and Modeling Tools for Human and Autonomous-Ready Driving Behaviors in Micro-Simulation. Smart Cities. 2021; 4 (1):314-335.
Chicago/Turabian StyleHafiz Ahmed; Ying Huang; Pan Lu. 2021. "A Review of Car-Following Models and Modeling Tools for Human and Autonomous-Ready Driving Behaviors in Micro-Simulation." Smart Cities 4, no. 1: 314-335.
Tracks are critical and expensive railroad asset, requiring frequent maintenance. The stress from heavy car axle loads increases the risk of deviations from uniform track geometry. Irregularities in track geometry, such as track warping, can cause an excessive harmonic rocking condition that can lead to derailments, traffic delays, and associated financial losses. This paper presents an approach to enhance the location identification accuracy of track geometry irregularities by combining measurements from sensors aboard Hi-Rail vehicles. However, speed variations, position recording errors, low GPS update rates, and the non-uniform sampling rates of inertial sensors pose significant challenges for signal processing, feature extraction, and signal combination. This study introduces a method of extracting features from the fused data of inertial sensors and GPS receivers with multiple traversals to locate and characterize irregularities of track geometry. The proposed method provides robust detection and enhanced accuracy in the localization of irregularities within spatial windows along the track segment. Tradeoff analysis found that the optimal spatial window size is 5-meter.
Bhavana Bhardwaj; Raj Bridgelall; Pan Lu; Neeraj Dhingra. Signal Feature Extraction and Combination to Enhance the Detection and Localization of Railroad Track Irregularities. IEEE Sensors Journal 2020, 21, 6555 -6563.
AMA StyleBhavana Bhardwaj, Raj Bridgelall, Pan Lu, Neeraj Dhingra. Signal Feature Extraction and Combination to Enhance the Detection and Localization of Railroad Track Irregularities. IEEE Sensors Journal. 2020; 21 (5):6555-6563.
Chicago/Turabian StyleBhavana Bhardwaj; Raj Bridgelall; Pan Lu; Neeraj Dhingra. 2020. "Signal Feature Extraction and Combination to Enhance the Detection and Localization of Railroad Track Irregularities." IEEE Sensors Journal 21, no. 5: 6555-6563.
This paper proposes a mathematical model, the competing risks method, to investigate highway-rail grade crossing (HRGC) crash frequency and crash severity simultaneously over a 30-year period. The proposed competing risks model is a special type of survival analysis to accommodate the competing nature of multiple outcomes from the same event of interest; in this case, the competing multiple outcomes are crash severities, while event of interest is crash occurrence. Knowledge-gain-based benefits to be discovered through the application of this model and 30-year dataset are as follows: (1) a straightforward and integrated one-step estimation process that considers both crash frequency and severity likelihood in the same model, so direct hazard ranking considering both crash frequency and severity likelihood is possible; (2) interpretative effects of identified covariates from both the direction and magnitude perspectives; and (3) the long-term cumulative effect of contributors with the cumulative incidence function.
Amin Keramati; Pan Lu; Xiaoyi Zhou; Denver Tolliver. A Simultaneous Safety Analysis of Crash Frequency and Severity for Highway-Rail Grade Crossings: The Competing Risks Method. Journal of Advanced Transportation 2020, 2020, 1 -13.
AMA StyleAmin Keramati, Pan Lu, Xiaoyi Zhou, Denver Tolliver. A Simultaneous Safety Analysis of Crash Frequency and Severity for Highway-Rail Grade Crossings: The Competing Risks Method. Journal of Advanced Transportation. 2020; 2020 ():1-13.
Chicago/Turabian StyleAmin Keramati; Pan Lu; Xiaoyi Zhou; Denver Tolliver. 2020. "A Simultaneous Safety Analysis of Crash Frequency and Severity for Highway-Rail Grade Crossings: The Competing Risks Method." Journal of Advanced Transportation 2020, no. : 1-13.
The remarkable mechanical properties and piezo-responses of carbon nanotubes (CNT) makes this group of nanomaterials an ideal candidate for use in smart cementitious materials to monitor forces and the corresponding structural health conditions of civil structures. However, the inconsistency in measurements is the major challenge of CNT-enabled smart cementitious materials to be widely applied for force detection. In this study, the modified tapioca starch co-polymer is introduced to surface treat the CNTs for a better dispersion of CNTs; thus, to reduce the inconsistency of force measurements of the CNTs modified smart cementitious materials. Cement mortar with bare (unmodified) CNTs (direct mixing method) and surfactant surface treated CNTs using sodium dodecyl benzenesulfonate (NaDDBS) were used as the control. The experimental results showed that when compared with samples made from bare CNTs, the samples made by modified tapioca starch co-polymer coated CNTs (CCNTs) showed higher dynamic load induced piezo-responses with significantly improved consistency and less hysteresis in the cementitious materials. When compared with the samples prepared with the surfactant method, the samples made by the developed CCNTs showed slightly increased force detection sensitivity with significantly improved consistency in piezo-response and only minor hysteresis, indicating enhanced dispersion effectiveness. The new CNT surface coating method can be scaled up easily to cater the potential industry needs for future wide application of smart cementitious materials.
Leonard Chia; Gina Blazanin; Ying Huang; Umma Salma Rashid; Pan Lu; Senay Simsek; Achintya N. Bezbaruah. Surface Treatment of Carbon Nanotubes Using Modified Tapioca Starch for Improved Force Detection Consistency in Smart Cementitious Materials. Sensors 2020, 20, 3985 .
AMA StyleLeonard Chia, Gina Blazanin, Ying Huang, Umma Salma Rashid, Pan Lu, Senay Simsek, Achintya N. Bezbaruah. Surface Treatment of Carbon Nanotubes Using Modified Tapioca Starch for Improved Force Detection Consistency in Smart Cementitious Materials. Sensors. 2020; 20 (14):3985.
Chicago/Turabian StyleLeonard Chia; Gina Blazanin; Ying Huang; Umma Salma Rashid; Pan Lu; Senay Simsek; Achintya N. Bezbaruah. 2020. "Surface Treatment of Carbon Nanotubes Using Modified Tapioca Starch for Improved Force Detection Consistency in Smart Cementitious Materials." Sensors 20, no. 14: 3985.
This paper proposes a machine learning approach, the random survival forest (RSF) for competing risks, to investigate highway-rail grade crossing (HRGC) crash severity during a 29-year analysis period. The benefits of the RSF approach are that it (1) is a special type of survival analysis able to accommodate the competing nature of multiple-event outcomes to the same event of interest (here the competing multiple events are crash severities), (2) is able to conduct an event-specific selection of risk factors, (3) has the capability to determine long-term cumulative effects of contributors with the cumulative incidence function (CIF), (4) provides high prediction performance, and (5) is effective in high-dimensional settings. The RSF approach is able to consider complexities in HRGC safety analysis, e.g., non-linear relationships between HRGCs crash severities and the contributing factors and heterogeneity in data. Variable importance (VIMP) technique is adopted in this research for selecting the most predictive contributors for each crash-severity level. Moreover, marginal effect analysis results real several HRGC countermeasures’ effectiveness. Several insightful findings are discovered. For examples, adding stop signs to HRGCs that already have a combination of gate, standard flashing lights, and audible devices will reduce the likelihood of property damage only (PDO) crashes for up to seven years; but after the seventh year, the crossings are more likely to have PDO crashes. Adding audible devices to crossing with gates and standard flashing lights will reduce crash likelihood, PDO, injury, and fatal crashes by 49 %, 52 %, 46 %, and 50 %, respectively.
Amin Keramati; Pan Lu; Amirfarrokh Iranitalab; Danguang Pan; Ying Huang. A crash severity analysis at highway-rail grade crossings: The random survival forest method. Accident Analysis & Prevention 2020, 144, 105683 .
AMA StyleAmin Keramati, Pan Lu, Amirfarrokh Iranitalab, Danguang Pan, Ying Huang. A crash severity analysis at highway-rail grade crossings: The random survival forest method. Accident Analysis & Prevention. 2020; 144 ():105683.
Chicago/Turabian StyleAmin Keramati; Pan Lu; Amirfarrokh Iranitalab; Danguang Pan; Ying Huang. 2020. "A crash severity analysis at highway-rail grade crossings: The random survival forest method." Accident Analysis & Prevention 144, no. : 105683.
Highway-rail grade crossing (HRGC) crashes continue to be the major contributors to rail causalities in the United States and have been intensively researched in the past. Data-mining models focus on prediction while dominant general linear models focus on model and data fitness. Decision makers and traffic engineers rely on prediction models to examine at-grade crash frequency and make safety improvement. The gradient boosting (GB) model has gained popularity in many research areas. In this study, to fully understand the model performance on HRGC accident prediction performance, the GB model with functional gradient descent algorithm is selected to analyze crashes at highway-rail grade crossings (HRGCs) and to identify contributor factors. Moreover, contributors’ importance and partial-dependent relations are generated to further understand the relationship of identified contributors and HRGC crash likelihood to concur “black box” issues that most machine learning methods face. Furthermore, to fully demonstrate the model’s prediction performance, a comprehensive model prediction power assessment based on six measurements is conducted, and the prediction performance of the GB model is verified and compared with a decision tree model as a reference due to their popularity and comparable data availability. It is demonstrated that the GB model produces better prediction accuracy and reveals nonlinear relationships among contributors and crash likelihood. In general, HRGC crash likelihood is significantly impacted by several traffic exposure factors: highway traffic volume, railway traffic volume, and train travel speed and others.
Pan Lu; Zijian Zheng; Yihao Ren; Xiaoyi Zhou; Amin Keramati; Denver Tolliver; Ying Huang. A Gradient Boosting Crash Prediction Approach for Highway-Rail Grade Crossing Crash Analysis. Journal of Advanced Transportation 2020, 2020, 1 -10.
AMA StylePan Lu, Zijian Zheng, Yihao Ren, Xiaoyi Zhou, Amin Keramati, Denver Tolliver, Ying Huang. A Gradient Boosting Crash Prediction Approach for Highway-Rail Grade Crossing Crash Analysis. Journal of Advanced Transportation. 2020; 2020 ():1-10.
Chicago/Turabian StylePan Lu; Zijian Zheng; Yihao Ren; Xiaoyi Zhou; Amin Keramati; Denver Tolliver; Ying Huang. 2020. "A Gradient Boosting Crash Prediction Approach for Highway-Rail Grade Crossing Crash Analysis." Journal of Advanced Transportation 2020, no. : 1-10.
Wood transportation costs have significant impacts on timber investment returns. To optimize transportation costs, a practical design of the route network is necessary for log trucks that ship wood material from timberlands to wood mills. Road conditions resulting from various road maintenance policies dramatically affect speed limits, log-truck route networks, and transportation costs. The effects of road maintenance policies on truck route selection and user/agency cost trade-offs have not been deeply researched. By considering forest-road maintenance policies, this study intends to provide accurate information on timber-processing service coverage and log-truck route selections for forest companies or subcontracted fleets of log trucks. This research also conducts an environmental analysis to assess the effects of various road maintenance policies on consumed energy and greenhouse gas emissions of log trucks. With current maintenance policies, the results indicate that only between 33% and 51% of timber areas in Oregon can be accessed or harvested by existing nearby wood mills. However, application of the proposed road maintenance policy in this paper would increase accessibility to timberlands by up to 104%. The cost of log transportation also decreases by 16%–34%. The methodology developed in this study can lead to improvements in forest road preservation management systems to achieve the full benefits of reducing log-truck emissions and total transportation costs.
Amin Keramati; Pan Lu; Ahmad Sobhani; Seyed Ali Haji Esmaeili. Impact of Forest Road Maintenance Policies on Log Transportation Cost, Routing, and Carbon-Emission Trade-Offs: Oregon Case Study. Journal of Transportation Engineering, Part A: Systems 2020, 146, 04020028 .
AMA StyleAmin Keramati, Pan Lu, Ahmad Sobhani, Seyed Ali Haji Esmaeili. Impact of Forest Road Maintenance Policies on Log Transportation Cost, Routing, and Carbon-Emission Trade-Offs: Oregon Case Study. Journal of Transportation Engineering, Part A: Systems. 2020; 146 (5):04020028.
Chicago/Turabian StyleAmin Keramati; Pan Lu; Ahmad Sobhani; Seyed Ali Haji Esmaeili. 2020. "Impact of Forest Road Maintenance Policies on Log Transportation Cost, Routing, and Carbon-Emission Trade-Offs: Oregon Case Study." Journal of Transportation Engineering, Part A: Systems 146, no. 5: 04020028.
Safety is a major concern of transportation planners and engineers in their design of highway rail grade crossings (HRGCs). Safety agencies rely on prediction models to allocate their crossing safety improvement resources. The prediction accuracy performance of those models is under-researched. This paper performs model forecasting accuracy comparison analysis for a proposed random forest method. Compared with the decision tree, the random forest method is capable of improving unbalanced data forecasting performance because of its bootstrap characteristic, which is a common resampling method to handle imbalanced data. Data imbalance is frequently encountered in safety analysis, where the use of inadequate performance metrics, such as accuracy, and specificity, will lead to overestimated generalization results. That is because the model/classifiers tend to predict the dominant class, non-crash class, in the area of safety analysis. The proposed random forest method is evaluated by various prediction performance measurements and compared with the decision tree. Results show that the random forest method dramatically improves the prediction accuracy without providing additional false negative predictions or false positive predictions which are known as false alarms.
Xiaoyi Zhou; Pan Lu; Zijian Zheng; Denver Tolliver; Amin Keramati. Accident Prediction Accuracy Assessment for Highway-Rail Grade Crossings Using Random Forest Algorithm Compared with Decision Tree. Reliability Engineering & System Safety 2020, 200, 106931 .
AMA StyleXiaoyi Zhou, Pan Lu, Zijian Zheng, Denver Tolliver, Amin Keramati. Accident Prediction Accuracy Assessment for Highway-Rail Grade Crossings Using Random Forest Algorithm Compared with Decision Tree. Reliability Engineering & System Safety. 2020; 200 ():106931.
Chicago/Turabian StyleXiaoyi Zhou; Pan Lu; Zijian Zheng; Denver Tolliver; Amin Keramati. 2020. "Accident Prediction Accuracy Assessment for Highway-Rail Grade Crossings Using Random Forest Algorithm Compared with Decision Tree." Reliability Engineering & System Safety 200, no. : 106931.
Highway-rail grade crossings (HRGCs) are where a roadway and railway intersect at the same level. Safety at HRGCs has been identified as a high-priority concern among transportation agencies, but there has been little research on the effects of HRGC geometric parameters on their safety performance. This paper evaluates the effects of HRGC geometric parameters on crash occurrence and severity likelihoods. The competing risk algorithm is selected to simultaneously analyze crash occurrence and severities. Four main HRGC geometric factors, along with other contributors, are investigated at 3,194 public HRGCs in North Dakota. This study focuses primarily on four geometric features of an HRGC: (1) acute crossing angle, (2) number of tracks (indicator of crossing width), (3) the roadway distance between the HRGC and the signalized intersection, and (4) number of highway lanes. Distance to the nearest roadway intersections and highway-railway crossing angles are map-based calculations drawn from geographic information systems (GIS). The findings are: (1) all contributors tested in this study, including highway characteristics, traffic exposures from both railway and highway, and the four geometric features, significantly affect at least one crash severity level; (2) all contributors significantly impact crash frequency except for the distance between crossings and the nearest roadway intersection; and (3) geometric parameters’ long-term effects on cumulative probability of crash severity and occurrence over 30 years is also evaluated. Crossings with three main tracks contribute the highest long-term crash probabilities.
Amin Keramati; Pan Lu; Denver Tolliver; Xingju Wang. Geometric effect analysis of highway-rail grade crossing safety performance. Accident Analysis & Prevention 2020, 138, 105470 .
AMA StyleAmin Keramati, Pan Lu, Denver Tolliver, Xingju Wang. Geometric effect analysis of highway-rail grade crossing safety performance. Accident Analysis & Prevention. 2020; 138 ():105470.
Chicago/Turabian StyleAmin Keramati; Pan Lu; Denver Tolliver; Xingju Wang. 2020. "Geometric effect analysis of highway-rail grade crossing safety performance." Accident Analysis & Prevention 138, no. : 105470.
Passenger Car Equivalent (PCE) is essential for transportation engineering to assess heavy vehicles’ (HV) impact on highway operations and capacity planning. Highway Capacity Manual 2010 (HCM 2010) used PCE values and percent of heavy vehicles to account the impacts on both highway planning and operation, however, PCE values in the latest version of HCM derived based on the steady and balanced two-lane-two-way (TLTW) traffic flows. The objective of the study is to identify PCE values for TLTW highway at various traffic volume with an emphasis on congestion conditions. This study introduces an analytical model, combining a headway-based and a delay-based algorithms, for estimating PCEs of HV on a TLTW highway. This study contributes to the literature by providing relationships among PCE, the traffic volume level (TVL) of both lanes, and the TVL duration on a TLTW highway. Traffic volume was categorized into five levels: TVL A (850 pc/h). The results indicate that on a TLTW highway, the TVLs of both lanes and their durations have significant impact on PCE values. In general, PCE values increase as TVL duration increases. Trucks have much higher impacts on operation under unbalanced conditions of TVL A with D, TVL B with C, and TVL D with B, when duration time is greater than one hour. When both lanes are saturated, trucks’ effect on capacity diminishes over time, and PCE values are approaching to 1.0.
Pan Lu; Zijian Zheng; Denver Tolliver; Danguang Pan. Measuring Passenger Car Equivalents (PCE) for Heavy Vehicle on Two Lane Highway Segments Operating Under Various Traffic Conditions. Journal of Advanced Transportation 2020, 2020, 1 -9.
AMA StylePan Lu, Zijian Zheng, Denver Tolliver, Danguang Pan. Measuring Passenger Car Equivalents (PCE) for Heavy Vehicle on Two Lane Highway Segments Operating Under Various Traffic Conditions. Journal of Advanced Transportation. 2020; 2020 ():1-9.
Chicago/Turabian StylePan Lu; Zijian Zheng; Denver Tolliver; Danguang Pan. 2020. "Measuring Passenger Car Equivalents (PCE) for Heavy Vehicle on Two Lane Highway Segments Operating Under Various Traffic Conditions." Journal of Advanced Transportation 2020, no. : 1-9.
The non-proportionally damped system is very common in practical engineering structures. The dynamic equations for these systems, in which the damping matrices are coupled, are very time consuming to solve. In this paper, a modal perturbation method is proposed, which only requires the first few lower real mode shapes of a corresponding undamped system to obtain the complex mode shapes of non-proportionally damped system. In this method, an equivalent proportionally damped system is constructed by taking the real mode shapes of a corresponding undamped system and then transforming the characteristic equation of state space into a set of nonlinear algebraic equations by using the vibration modes of an equivalent proportionally damped system. Two numerical examples are used to illustrate the validity and accuracy of the proposed modal perturbation method. The numerical results show that: (1) with the increase of vibration modes of the corresponding undamped system, the eigenvalues and eigenvectors monotonically converge to exact solutions; (2) the accuracy of the proposed method is significantly higher than the first-order perturbation method and proportional damping method. The calculation time of the proposed method is shorter than the state space method; (3) the method is particularly suitable for finding a few individual orders of frequency and mode of a system with highly non-proportional damping.
Danguang Pan; Xiangqiu Fu; Qingjun Chen; Pan Lu; Jinpeng Tan. A Modal Perturbation Method for Eigenvalue Problem of Non-Proportionally Damped System. Applied Sciences 2020, 10, 341 .
AMA StyleDanguang Pan, Xiangqiu Fu, Qingjun Chen, Pan Lu, Jinpeng Tan. A Modal Perturbation Method for Eigenvalue Problem of Non-Proportionally Damped System. Applied Sciences. 2020; 10 (1):341.
Chicago/Turabian StyleDanguang Pan; Xiangqiu Fu; Qingjun Chen; Pan Lu; Jinpeng Tan. 2020. "A Modal Perturbation Method for Eigenvalue Problem of Non-Proportionally Damped System." Applied Sciences 10, no. 1: 341.
Frequent network-wide monitoring of the condition of roadways and railways prevent fatalities, injuries, and financial losses. Even so, agencies cannot afford to inspect vast transportation networks using present methods. Therefore, the idea of using low-cost sensors aboard connected vehicles became appealing. However, low-cost sensors introduce new challenges to improve poor signal quality which causes detection errors. Common approaches apply computationally complex filters to individual signal streams, which limits further improvements. This paper presents a method that combines signals from each traversal in a manner that leads to ever-increasing signal quality. The proposed method addresses the challenges of poor accuracy and precision of position estimates from global positioning system (GPS) receivers, and errors from the non-uniform sampling of low-cost accelerometers. The result is improved signal quality from a 20% improvement in signal alignment over GPS and a 90-fold enhancement in distance precision.
Raj Bridgelall; Leonard A Chia; Bhavana Bhardwaj; Pan Lu; Denver D Tolliver; Neeraj Dhingra. Enhancement of signals from connected vehicles to detect roadway and railway anomalies. Measurement Science and Technology 2019, 31, 035105 .
AMA StyleRaj Bridgelall, Leonard A Chia, Bhavana Bhardwaj, Pan Lu, Denver D Tolliver, Neeraj Dhingra. Enhancement of signals from connected vehicles to detect roadway and railway anomalies. Measurement Science and Technology. 2019; 31 (3):035105.
Chicago/Turabian StyleRaj Bridgelall; Leonard A Chia; Bhavana Bhardwaj; Pan Lu; Denver D Tolliver; Neeraj Dhingra. 2019. "Enhancement of signals from connected vehicles to detect roadway and railway anomalies." Measurement Science and Technology 31, no. 3: 035105.
Mu'ath Al-Tarawneh; Ying Huang; Pan Lu; Raj Bridgelall. Weigh-In-Motion System in Flexible Pavements Using Fiber Bragg Grating Sensors Part A: Concept. IEEE Transactions on Intelligent Transportation Systems 2019, 21, 5136 -5147.
AMA StyleMu'ath Al-Tarawneh, Ying Huang, Pan Lu, Raj Bridgelall. Weigh-In-Motion System in Flexible Pavements Using Fiber Bragg Grating Sensors Part A: Concept. IEEE Transactions on Intelligent Transportation Systems. 2019; 21 (12):5136-5147.
Chicago/Turabian StyleMu'ath Al-Tarawneh; Ying Huang; Pan Lu; Raj Bridgelall. 2019. "Weigh-In-Motion System in Flexible Pavements Using Fiber Bragg Grating Sensors Part A: Concept." IEEE Transactions on Intelligent Transportation Systems 21, no. 12: 5136-5147.
Bhavana Bhardwaj; Raj Bridgelall; Leonard Chia; Pan Lu; Neeraj Dhingra. Signal Filter Cut-Off Frequency Determination to Enhance the Accuracy of Rail Track Irregularity Detection and Localization. IEEE Sensors Journal 2019, 20, 1393 -1399.
AMA StyleBhavana Bhardwaj, Raj Bridgelall, Leonard Chia, Pan Lu, Neeraj Dhingra. Signal Filter Cut-Off Frequency Determination to Enhance the Accuracy of Rail Track Irregularity Detection and Localization. IEEE Sensors Journal. 2019; 20 (3):1393-1399.
Chicago/Turabian StyleBhavana Bhardwaj; Raj Bridgelall; Leonard Chia; Pan Lu; Neeraj Dhingra. 2019. "Signal Filter Cut-Off Frequency Determination to Enhance the Accuracy of Rail Track Irregularity Detection and Localization." IEEE Sensors Journal 20, no. 3: 1393-1399.
Soil mixtures with various materials such as scraps of rubber tire, iron powder, and synthetic fibers have been widely used in civil engineering for experimental research or infrastructure construction and maintenance. However, these materials are not only expensive, but may also result in environmental concerns. In recent years, sawdust, because of its light-weight, inexpensive, and environmental friendly characteristics, has frequently been used in the shaking table test to adjust the dynamic properties of experimental soil. However, the dynamic properties of a sand-sawdust mixture for the shaking table test are still unclear. In this paper, the dynamic properties and the hysteresis curve characteristics of the sand-sawdust mixture as well as the influence of the sawdust content and confining pressure on the dynamic properties were studied using a series of consolidated drained dynamic triaxial tests. The test results show that, with the increase of the shear strain, the shape of the hysteresis loops changes from symmetrical willow-leaf to asymmetry sharp-leaf. For a given confining pressure, both the shear modulus and damping ratio decreases as the sawdust percentage increases. It was observed that, with an increase in confining pressure, the shear modulus increased while the damping ratio decreased slightly in the shear strain range of 10−3 to 7×10−3. It was also observed that the maximum shear modulus increased as the confining pressure increased, while the maximum damping ratio remained nearly constant. In addition, both the maximum shear modulus and the maximum damping ratio decreased as the sawdust content increased. Finally, the normalized shear modulus and damping ratio were established, which can be used in simulations using the shaking table test.
Danguang Pan; Xueju Li; Pan Lu; Qingjun Chen; Pan; Li; Lu; Chen. Dynamic Properties of Sand-Sawdust Mixture for Modeling Deposit Soil. Applied Sciences 2019, 9, 3863 .
AMA StyleDanguang Pan, Xueju Li, Pan Lu, Qingjun Chen, Pan, Li, Lu, Chen. Dynamic Properties of Sand-Sawdust Mixture for Modeling Deposit Soil. Applied Sciences. 2019; 9 (18):3863.
Chicago/Turabian StyleDanguang Pan; Xueju Li; Pan Lu; Qingjun Chen; Pan; Li; Lu; Chen. 2019. "Dynamic Properties of Sand-Sawdust Mixture for Modeling Deposit Soil." Applied Sciences 9, no. 18: 3863.
B. Bhardwaj; R. Bridgelall; Pan Lu; N. Dhingra. Railroad Track Irregularities Position Accuracy Assessments Using Low-Cost Sensors on a Hi-Rail Vehicle. International Conference on Transportation and Development 2019 2019, 1 .
AMA StyleB. Bhardwaj, R. Bridgelall, Pan Lu, N. Dhingra. Railroad Track Irregularities Position Accuracy Assessments Using Low-Cost Sensors on a Hi-Rail Vehicle. International Conference on Transportation and Development 2019. 2019; ():1.
Chicago/Turabian StyleB. Bhardwaj; R. Bridgelall; Pan Lu; N. Dhingra. 2019. "Railroad Track Irregularities Position Accuracy Assessments Using Low-Cost Sensors on a Hi-Rail Vehicle." International Conference on Transportation and Development 2019 , no. : 1.
Leonard Chia; Pan Lu; Bhavana Bhardwaj; Raj Bridgelall; Denver Tolliver; Neeraj Dhingra. Automatic Rail Track Surface Anomaly Detection with Smartphone Based Monitoring System. DEStech Transactions on Engineering and Technology Research 2019, 1 .
AMA StyleLeonard Chia, Pan Lu, Bhavana Bhardwaj, Raj Bridgelall, Denver Tolliver, Neeraj Dhingra. Automatic Rail Track Surface Anomaly Detection with Smartphone Based Monitoring System. DEStech Transactions on Engineering and Technology Research. 2019; (icicr):1.
Chicago/Turabian StyleLeonard Chia; Pan Lu; Bhavana Bhardwaj; Raj Bridgelall; Denver Tolliver; Neeraj Dhingra. 2019. "Automatic Rail Track Surface Anomaly Detection with Smartphone Based Monitoring System." DEStech Transactions on Engineering and Technology Research , no. icicr: 1.