Highway Regional Classification Method Based on Traffic Flow Characteristics for Highway Safety Assessment

Accurate regional classification of highways is a critical prerequisite to implement a tailored safety assessment. However, there has been inadequate research on objective classification considering traffic flow characteristics for highway safety assessment purposes. We propose an objective and easily applicable classification method that considers the administrative divisions of South Korea. We evaluated the feasibility of this method through various theoretical analysis techniques using the data collected from 536 permanent traffic volume counting stations for the national highways in South Korea in 2019. The ratio of the annual average hourly traffic volume to the annual average daily traffic was used as the explanatory variable. The corresponding results of factor and cluster analyses with this ratio showed a 61% concordance with the urban, suburban, and rural areas classified by the administrative divisions. The results of two-sample goodness-of-fit tests also confirmed that the difference in the three distributions of hourly volume ratios was statistically significant. The results of this study can help enhance highway safety and facilitate the development and application of more appropriate highway safety assessment tools, such as Road Assessment Programs or crash prediction models, for specific regions using the proposed method.</jats:p

Highway Regional Classification Method Based on Traffic Flow Characteristics for Highway Safety Assessment

Accurate regional classification of highways is a critical prerequisite to implement a tailored safety assessment. However, there has been inadequate research on objective classification considering traffic flow characteristics for highway safety assessment purposes. We propose an objective and easily applicable classification method that considers the administrative divisions of South Korea. We evaluated the feasibility of this method through various theoretical analysis techniques using the data collected from 536 permanent traffic volume counting stations for the national highways in South Korea in 2019. The ratio of the annual average hourly traffic volume to the annual average daily traffic was used as the explanatory variable. The corresponding results of factor and cluster analyses with this ratio showed a 61% concordance with the urban, suburban, and rural areas classified by the administrative divisions. The results of two-sample goodness-of-fit tests also confirmed that the difference in the three distributions of hourly volume ratios was statistically significant. The results of this study can help enhance highway safety and facilitate the development and application of more appropriate highway safety assessment tools, such as Road Assessment Programs or crash prediction models, for specific regions using the proposed method

Two-Dimensional LiDAR Sensor-Based Three-Dimensional Point Cloud Modeling Method for Identification of Anomalies inside Tube Structures for Future Hypersonic Transportation

The hyperloop transportation system has emerged as an innovative next-generation transportation system. In this system, a capsule-type vehicle inside a sealed near-vacuum tube moves at 1000 km/h or more. Not only must this transport tube span over long distances, but it must be clear of potential hazards to vehicles traveling at high speeds inside the tube. Therefore, an automated infrastructure anomaly detection system is essential. This study sought to confirm the applicability of advanced sensing technology such as Light Detection and Ranging (LiDAR) in the automatic anomaly detection of next-generation transportation infrastructure such as hyperloops. To this end, a prototype two-dimensional LiDAR sensor was constructed and used to generate three-dimensional (3D) point cloud models of a tube facility. A technique for detecting abnormal conditions or obstacles in the facility was used, which involved comparing the models and determining the changes. The design and development process of the 3D safety monitoring system using 3D point cloud models and the analytical results of experimental data using this system are presented. The tests on the developed system demonstrated that anomalies such as a 25 mm change in position were accurately detected. Thus, we confirm the applicability of the developed system in next-generation transportation infrastructure.</jats:p

Two-Dimensional LiDAR Sensor-Based Three-Dimensional Point Cloud Modeling Method for Identification of Anomalies inside Tube Structures for Future Hypersonic Transportation

The hyperloop transportation system has emerged as an innovative next-generation transportation system. In this system, a capsule-type vehicle inside a sealed near-vacuum tube moves at 1000 km/h or more. Not only must this transport tube span over long distances, but it must be clear of potential hazards to vehicles traveling at high speeds inside the tube. Therefore, an automated infrastructure anomaly detection system is essential. This study sought to confirm the applicability of advanced sensing technology such as Light Detection and Ranging (LiDAR) in the automatic anomaly detection of next-generation transportation infrastructure such as hyperloops. To this end, a prototype two-dimensional LiDAR sensor was constructed and used to generate three-dimensional (3D) point cloud models of a tube facility. A technique for detecting abnormal conditions or obstacles in the facility was used, which involved comparing the models and determining the changes. The design and development process of the 3D safety monitoring system using 3D point cloud models and the analytical results of experimental data using this system are presented. The tests on the developed system demonstrated that anomalies such as a 25 mm change in position were accurately detected. Thus, we confirm the applicability of the developed system in next-generation transportation infrastructure

Collision Models for Multilane Highway Segments Incorporating the Effects of Curbs

The main objective of this study was to develop valid statistical collision models for multilane highway segments with or without curbs. For this, road geometric data, traffic data, and collision data for the three years were collected. The data include 2,274 collisions and 885 injury collisions that occurred on 191.85 miles of 199 directional segments. A new modeling method of introducing variables into the model one by one in a multiplicative form was applied. A nonlinear optimizing algorithm for estimating parameters using a negative binomial log likelihood function was adopted for the modeling. The functional form of the variable to be introduced was determined based on the relationship between the recorded number of collisions and the number of collisions predicted by the current model without the variable. The integrate-differentiate method was applied to find candidate functional forms for each variable. Model selections were based on the -2 log likelihood and BIC statistics. The cumulative residuals (CURE) plot method was adopted for checking the goodness of fit of the models. As a result of the modeling efforts, the annual average daily traffic, access point density, shoulder width, and shoulder type variables were introduced to the final model for total collisions. The same variables except the shoulder type variable were introduced to the injury collision model. Overall, then, it appears that curbs mean fewer total collisions and no change in injury collisions as compared to no curbs on the sampled road segments. The models developed in this study were based only on the data for North Carolina and limited number of variables. The developed models can be improved in the future by collecting data on more miles, by bringing more explanatory variables into models, and by using the data from other states. Additionally, the characteristics of vehicles speeds on multilane highways were analyzed and compared. The results showed that the mean speeds for the non-curbed sites were about 2 to 3 mph higher than those for the curbed sites

Adhesive Defect Monitoring of Glass Fiber Epoxy Plate Using an Impedance-Based Non-Destructive Testing Method for Multiple Structures

The emergence of composite materials has revolutionized the approach to building engineering structures. With the number of applications for composites increasing every day, maintaining structural integrity is of utmost importance. For composites, adhesive bonding is usually the preferred choice over the mechanical fastening method, and monitoring for delamination is an essential factor in the field of composite materials. In this study, a non-destructive method known as the electromechanical impedance method is used with an approach of monitoring multiple areas by specifying certain frequency ranges to correspond to a certain test specimen. Experiments are conducted using various numbers of stacks created by attaching glass fiber epoxy composite plates onto one another, and two different debonding damage types are introduced to evaluate the performance of the multiple monitoring electromechanical impedance method

Adhesive Defect Monitoring of Glass Fiber Epoxy Plate Using an Impedance-Based Non-Destructive Testing Method for Multiple Structures

The emergence of composite materials has revolutionized the approach to building engineering structures. With the number of applications for composites increasing every day, maintaining structural integrity is of utmost importance. For composites, adhesive bonding is usually the preferred choice over the mechanical fastening method, and monitoring for delamination is an essential factor in the field of composite materials. In this study, a non-destructive method known as the electromechanical impedance method is used with an approach of monitoring multiple areas by specifying certain frequency ranges to correspond to a certain test specimen. Experiments are conducted using various numbers of stacks created by attaching glass fiber epoxy composite plates onto one another, and two different debonding damage types are introduced to evaluate the performance of the multiple monitoring electromechanical impedance method

Piezoelectric Impedance-Based Non-Destructive Testing Method for Possible Identification of Composite Debonding Depth

Detecting the depth and size of debonding in composite structures is essential for assessing structural safety as it can weaken the structure possibly leading to a failure. As composite materials are used in various fields up to date including aircrafts and bridges, inspections are carried out to maintain structural integrity. Although many inspection methods exist for detection damage of composites, most of the techniques require trained experts or a large equipment that can be time consuming. In this study, the possibility of using the piezoelectric material-based non-destructive method known as the electromechanical impedance (EMI) technique is used to identify the depth of debonding damage of glass epoxy laminates. Laminates with various thicknesses were prepared and tested to seek for the possibility of using the EMI technique for identifying the depth of debonding. Results show promising outcome for bringing the EMI technique a step closer for commercialization.</jats:p

Impedance Based Health Monitoring Technique with Probabilistic Neural Network for Possible Wall Thinning Detection of Metal Structures

Corrosion of structures and wall thinning of pipes can severely affect the mechanical strength as wall thickness is reduced. Thus a cost effective structural health monitoring technique plays an important role when managing a structure. The electromechanical impedance (EMI) method is a local method that has limited sensing range, resulting in a high cost when covering large areas. In this study, a reattachable EMI method is investigated using a stack of multiple metal plates to conduct an experiment involving thickness reduction. In addition, the main problem of the impedance signatures changing subjected to reattaching the piezoelectric transducer is solved by using the probabilistic neural network algorithm presented for the study. The proposed approach successfully identifies the thickness of two different structures with high accuracy

A Review of the Piezoelectric Electromechanical Impedance Based Structural Health Monitoring Technique for Engineering Structures

The birth of smart materials such as piezoelectric (PZT) transducers has aided in revolutionizing the field of structural health monitoring (SHM) based on non-destructive testing (NDT) methods. While a relatively new NDT method known as the electromechanical (EMI) technique has been investigated for more than two decades, there are still various problems that must be solved before it is applied to real structures. The technique, which has a significant potential to contribute to the creation of one of the most effective SHM systems, involves the use of a single PZT for exciting and sensing of the host structure. In this paper, studies applied for the past decade related to the EMI technique have been reviewed to understand its trend. In addition, new concepts and ideas proposed by various authors are also surveyed, and the paper concludes with a discussion of the potential directions for future works