Over-height Vehicles Research Articles

Smart Rumble Strip System to Prevent Over-Height Vehicle Collisions.

Collisions of over-height vehicles with low clearance bridges is commonly encountered worldwide. They have caused damage to bridge structures, interruption to traffic, injuries or even fatalities to road users. To mitigate such risks, passive systems that involve warning gantries, flashing lights and illuminated signage are commonly installed. Semi-active systems using laser- or infrared-based detection systems in conjunction with visual warnings have been implemented. Nevertheless, some drivers ignore these visual warnings and collisions continue to occur. This paper presents a novel concept for a collision prevention system, which makes use of a series of sensor-activated, motorized rumble strips. These rumble strips span across a certain distance ahead of a low clearance bridge. When an over-height vehicle is detected, a mechanism is triggered which elevates the rumble strips. The noise and vibrations produce a vigorous alert to the offending driver. They also increase effective friction of the road surface, thus assisting to slow down the vehicle and shorten the stopping distance. The strips will be lowered after a certain time has elapsed, thus minimizing their effects on other vehicles. This article presents a conceptual framework and quantifies the vibration and noise caused by rumble strips in road tests. Road tests indicated that the vibration level typically exceeded 1 g and noise level reached approximately 90 dB in the cabin of a 3.5-ton truck. Fabrication of a proof-of-concept mechanized rumble strip model was presented and verified in an outdoor environment. The circuitry and mechanical design, and requirements in actual implementation, are discussed. The proposed event-triggered rumble strip system could significantly mitigate over-height vehicle collisions that cause major disruptions and injuries worldwide. Further works, including a comprehensive road test involving various types of vehicles, are envisaged.

Edge Integration of Artificial Intelligence into Wireless Smart Sensor Platforms for Railroad Bridge Impact Detection.

Of the 100,000 railroad bridges in the United States, 50% are over 100 years old. Many of these bridges do not meet the minimum vertical clearance standards, making them susceptible to impact from over-height vehicles. The impact can cause structural damage and unwanted disruption to railroad bridge services; rapid notification of the railroad authorities is crucial to ensure that the bridges are safe for continued use and to affect timely repairs. Therefore, researchers have developed approaches to identify these impacts on railroad bridges. Some recent approaches use machine learning to more effectively identify impacts from the sensor data. Typically, the collected sensor data are transmitted to a central location for processing. However, the challenge with this centralized approach is that the transfer of data to a central location can take considerable time, which is undesirable for time-sensitive events, like impact detection, that require a rapid assessment and response to potential damage. To address the challenges posed by the centralized approach, this study develops a framework for edge implementation of machine-learning predictions on wireless smart sensors. Wireless sensors are used because of their ease of installation and lower costs compared to their wired counterparts. The framework is implemented on the Xnode wireless smart sensor platform, thus bringing artificial intelligence models directly to the sensor nodes and eliminating the need to transfer data to a central location for processing. This framework is demonstrated using data obtained from events on a railroad bridge near Chicago; results illustrate the efficacy of the proposed edge computing framework for such time-sensitive structural health monitoring applications.

Parallel heterogeneous data‐fusion convolutional neural networks for improved rail bridge strike detection

AbstractLow clearance rail bridges provide vital crossings for freight and passenger trains and are susceptible to frequent strikes from overheight vehicles or equipment. Impact detection systems can help ensure the safety of railroad bridges and their users; such systems streamline monitoring efforts by providing near real‐time strike notifications to rail managers responsible for assessing a bridge after a strike. This paper develops parallel heterogeneous data‐fusion convolutional neural networks (PHD‐CNN) operating on data collected from in‐service rail bridges that improves detection and classification of from overheight vehicles. Convolutional neural networks (CNNs) automatically extract features from multiple data streams from different sensor modalities. The method provides a mechanism to homogenize and fuse disparate data streams for use as inputs to a classifier that distinguishes bridge strikes from passing trains. The study also provides practical implementation guidelines through a framework sensitivity characterization to examine the effects on performance of input data stream type, data set size, and CNN architecture complexity. Optimum networks detect, on average, 95% of bridge strikes with false positive rates less than 2%.

Damage mechanism and damage evaluation of PC box girder bridge subjected to overheight vehicle impact

AbstractAccidental collisions from overheight vehicles often result in significant damage of bridges. To ensure the safety of the bridge structure, it is essential to develop a method to evaluate the damage caused by these impact loadings. This study aims to investigate the damage mechanism of a prestressed concrete (PC) box girder bridge subjected to overheight vehicle impact using the finite element analysis method. Parametric analyses were performed to study the effects of various factors on the impact force and deformation performance of the bridge. In addition, a damage assessment formula, based on the vertical residual bearing capacity, is proposed to evaluate the damage state of the impacted PC box girder. A mass–velocity (m–v) curve is developed to characterize the damage levels of the impacted PC girder under different vehicle velocity and mass conditions. Then, a theoretical equation for predicting the maximum impact force during the vehicle‐bridge collision is developed. The numerical analysis results show that vehicle‐related parameters have a significant influence on both impact force and local damage of the PC box girder bridge. The proposed mass–velocity (m–v) curve can be used to evaluate the damage state of the PC box girder by relating the performance levels of the girder to each damage index. Moreover, the predicted maximum impact force can be used as an important index to assess the extent of the local damage of the PC box girder.

An Event-Classification Neural Network Approach for Rapid Railroad Bridge Impact Detection.

Railroads are a critical part of the United States' transportation sector. Over 40 percent (by weight) of the nation's freight is transported by rail, and according to the Bureau of Transportation statistics, railroads moved $186.5 billion of freight in 2021. A vital part of the freight network is railroad bridges, with a good number being low-clearance bridges that are prone to impacts from over-height vehicles; such impacts can cause damage to the bridge and lead to unwanted interruption in its usage. Therefore, the detection of impacts from over-height vehicles is critical for the safe operation and maintenance of railroad bridges. While some previous studies have been published regarding bridge impact detection, most approaches utilize more expensive wired sensors, as well as relying on simple threshold-based detection. The challenge is that the use of vibration thresholds may not accurately distinguish between impacts and other events, such as a common train crossing. In this paper, a machine learning approach is developed for accurate impact detection using event-triggered wireless sensors. The neural network is trained with key features which are extracted from event responses collected from two instrumented railroad bridges. The trained model classifies events as impacts, train crossings, or other events. An average classification accuracy of 98.67% is obtained from cross-validation, while the false positive rate is minimal. Finally, a framework for edge classification of events is also proposed and demonstrated using an edge device.

Stress change calculations in accidentally damaged prestressed bridge girders

Accidental damage to prestressed concrete bridge girders may occur due to impact by over-height vehicles on the bottom of the girder, or the top flange of the girder may be damaged during deck removal and replacement operations. Since stress checks at service loads are an important component of design for prestressed concrete beams, serviceability-based stress checks should be considered when assessing the structural condition of damaged girders. In this paper, step-by-step theoretical calculations are used to develop equations for estimating service load stress changes due to physical damage (partial loss of concrete section and strands) based on a differential approach. The effects of lack of symmetry in the damaged cross section is considered in the calculations. A spreadsheet-based program is developed to calculate complex section property values for the damaged sections. The accuracy of the developed equations was verified using a finite element model of a prestressed beam under undamaged and damaged conditions. Reasonably good agreement was noted between the predicted stress changes and the finite element results.

XImpact: Intelligent Wireless System for Cost-Effective Rapid Condition Assessment of Bridges under Impacts.

Bridge strikes by over-height vehicles or ships are critical sudden events. Due to their unpredictable nature, many events go unnoticed or unreported, but they can induce structural failures or hidden damage that accelerates the bridge’s long-term degradation. Therefore, always-on monitoring is essential for deployed systems to enhance bridge safety through the reliable detection of such events and the rapid assessment of bridge conditions. Traditional bridge monitoring systems using wired sensors are too expensive for widespread implementation, mainly due to their significant installation cost. In this paper, an intelligent wireless monitoring system is developed as a cost-effective solution. It employs ultralow-power, event-triggered wireless sensor prototypes, which enables on-demand, high-fidelity sensing without missing unpredictable impact events. Furthermore, the proposed system adopts a smart artificial intelligence (AI)-based framework for rapid bridge assessment by utilizing artificial neural networks. Specifically, it can identify the impact location and estimate the peak force and impulse of impacts. The obtained impact information is used to provide early estimation of bridge conditions, allowing the bridge engineers to prioritize resource allocation for the timely inspection of the more severe impacts. The performance of the proposed monitoring system is demonstrated through a full-scale field test. The test results show that the developed system can capture the onset of bridge impacts, provide high-quality synchronized data, and offer a rapid damage assessment of bridges under impact events, achieving the error of around 2 m in impact localization, 1 kN for peak force estimation, and 0.01 kN·s for impulse estimation. Long-term deployment is planned in the future to demonstrate its reliability for real-life impact events.

Ultrahigh-Performance Concrete for Improving Impact Resistance of Bridge Superstructures to Overheight Collision

Abstract Collision of overheight vehicles into bridge superstructures often leads to structural damage, posing an immediate risk to the safety of motorists and the functionality of bridges. Conside...

Characterization of impact-induced forces and damage to bridge superstructures due to over-height collision

Over-height collisions into bridge superstructures often lead to structural damage, posing an immediate risk to the safety of motorists and functionality of bridges. To ensure that the bridge girders impacted by over-height vehicles are not compromised, it is essential to have an accurate assessment of the impact-induced loading demand. The current codes and specifications, however, lack the information necessary to estimate the impact forces that the bridge girders can experience during an over-height collision. To address this gap, a detailed investigation is conducted in the current study to (i) characterize the loading demand expected from various impacting objects, (ii) determine the range and variability of impact-induced forces (as a function of the velocity and size of the impacting objects), and (iii) develop a set of equations to predict the impact forces from over-height collisions into bridge superstructures. For this purpose, high-fidelity finite-element (FE) models are developed, covering four individual objects, i.e., concrete pipe, steel tank, PVC pipe, and wooden box container, in addition to a tractor-semitrailer. The structural performance of the bridge superstructures impacted by the identified objects is evaluated using several response measures, including impact force, internal shear, and damage pattern. The outcome of this study provides the input necessary for the design of new and the assessment of existing bridge superstructures against possible over-height collisions.

The over-height vehicle detection using the computer vision method

Over-height vehicles are not only threats for the over-height vehicles themselves, but they also bring about a hazard to the tunnels they pass in, and to other drivers on the road way when a clash happens with a low clearance structure. Therefore, it is needed an over-height detection system which is affordable yet also reliable. Nowadays, there are the over-height detection system using laser and infrared. However, they are quite pricy. In this research a computer vision-based system is proposed to detect the height of vehicles and provide a warning for the over-height vehicles. The height determination was conducted using Gaussian Mixture Model (GMM) and blob detection. The GMM is used to detect the vehicle and the blob detection is applied to produce the vehicle coordinates which determines the direction of movement and the over-height vehicle. The over-height vehicles are detected by using 5 (five) variance total frames within 3 (three) conditions. The accuracy of the test proves that the method is reliable in determining the height of vehicles, achieving 100% accuracy of the detected vehicles. The significance of this work is the design of a vision-based method which can determine the height of the vehicles and is a low-cost alternative to the current costly laser and infrared detection systems.

Design and field implementation of an impact detection system using committees of neural networks

Many critical societal functions depend on uninterrupted service of civil engineering infrastructure. Railroads represent important infrastructure components of the transportation sector and provide both passenger and freight services. Railroad bridges over roadways are susceptible to impacts from overheight vehicles and equipment, which may damage bridge girders or supports and must be investigated after each event. One method of monitoring for vehicle-bridge collisions utilizes accelerometers to monitor for abnormal bridge vibrations corresponding to abnormal activity. Passing trains under normal operating conditions frequently produce significant bridge responses that have similar response characteristics to bridge strikes, but do not need to be investigated. This paper presents an expert system which comprises committees of artificial neural networks trained to interrogate data collected from accelerometers mounted on the bridge, assess the nature of the acceleration signal, and classify the event as either a passing train or a potentially damaging impact. This system is trained using acceleration time histories from accelerometers installed on 8 low-clearance rail bridges; no finite element model simulations were used for network training or data stream creation. The presented system accurately detects and classifies impacts with average impact detection performance ranging from 91–100% with average false positive rates limited to 0.00–0.75%.

Real-time validation of vision-based over-height vehicle detection system

Over-height vehicle strikes with low bridges and tunnels are an ongoing problem worldwide. While previous methods have used vision-based systems to address the over-height warning problem, such methods are sensitive to wind. In this paper, we perform a full validation of the system using a constraint-based approach to minimize the number of over-height vehicle misclassifications due to windy conditions. The dataset includes a total of 102 over-height vehicles recorded at frame rates of 25 and 30fps. An analysis is performed of wind and vehicle displacements to track over-height features using optical flow paired with SURF feature detectors. Motion captured within the region of interest was treated as a standard two-class binary linear classification problem with 1 indicating over-height vehicle presence and 0 indicating noise. The algorithm performed with 100% recall, 83.3% precision, false positive rate of 0.2% and warning accuracy of 96.6%.

LADAR‐based collision warning sensor to prevent over‐height vehicle bridge hits

Impacts by over-height (OH) vehicles on bridges, commonly known as `bridge hits', cause significant risk to safety and preservation of transportation infrastructure in the USA. Currently, available over-height vehicle detection systems (OHVDs) have specific site requirements, extremely high installation costs, and propensity for false alarms, which limit their field deployment to few locations. This study describes a new, enhanced LADAR-based OHVDs (L-OHVDs), which can be installed on the face of a structure to be protected and can measure the height of an approaching vehicle before the safe stopping distance from the structure. Built using off-the-shelf components and a patent pending optical design, it exhibits enhanced features like vehicle detection, actual height measurement, and collision prediction with no reported false alarms. The developed prototype has a detection range of 220 ft with height measurement accuracy (±0.66 inches) that is better than currently available OHVDs. This system has exceptional precision and is well suited to detect OH trucks and tractor trailers approaching a low vertical clearance bridge. With superior performance and cost-effective installation, the proposed L-OHVDs has the potential to reduce occurrences of `bridge hits', thereby limiting consequences such as congestion and damages to bridge while sustaining safety of motorists.

Network Level Clearance Assessment using LiDAR to Improve the Reliability and Efficiency of Issuing Over-Height Permits on Highways

Commercial vehicles on highway networks are only permitted to use routes that are designed to accommodate their sizes and weights. When issuing an over-height permit, agencies consider vertical clearances on all route options on a highway network before directing a vehicle to the optimal route. Inefficient routing of commercial vehicles could cause excessive delays, which would result in undesirable economic impacts. Moreover, inaccurate assignment of vehicles to routes where the road infrastructure cannot handle the vehicle’s size could cause significant damage to the roadway, resulting in potential safety risks. Although routing programs and permit-issuing agencies try to avoid inaccurate assignments, this is not always possible since they rely on a database of information prone to human error and one which is not always up to date. To create a more reliable database of information, Departments of Transport need more efficient methods to collect information on highways. This paper aims to increase the efficiency of collecting data required to issue over-height permits by utilizing LiDAR data to automatically assess vertical clearance on highways. The method used involves detecting all overhead objects on a highway corridor and estimating the clearance at each object before mapping the data on a GIS map. The method was tested on three different highway corridors in Alberta, Canada ranging in length from 130 to 400 km. Testing revealed that the proposed method is effective in performing network-level assessment of vertical clearance, which has significant impacts on the efficiency of routing over-height vehicles on a network.

Rehabilitation of a Vehicle Impact Damaged Concrete Bridge Girder with GFRP Rebars

Overpass bridge girders are susceptible to impact damage of over-height vehicles, creating a traffic hazard and structural deficiency. The repair for a damaged girder has to meet adequate criteria for the safety, repair time and economy. This paper presents a case study for the repair of such an impact damaged concrete girder on the Lyndon B. Johnson Express construction project, located on I-635 and I-35 freeways in Dallas, Texas. The impact caused concrete loss and exposed several restressing strands on the exterior girder. The overpass had been completed while the old route was open below, causing a temporary vertical lower clearance than the final design, leading to the impact. The novel and innovative repair process involved fiber glass (GFRP) rebars, bonding epoxy and repair mortar. These rebars enhanced the flexural capacity of the repaired section and supplemented the mortar strength. Onsite load testing was employed to verify the performance of the repaired structure. Theoretical model of the composite girder before and after repair was employed. The strain data from the model compared well with the load testing data. The repair scheme drastically increased the stiffness of the damaged girder, resulting in about 50% reduction in the bottom strains. The beneficial effect of the repair resulted in large increases in the net compressive stresses (200–300%) at the girder bottom through the increase of the section stiffness and reduction of the gravity load stresses. Stresses remained well below the elastic range for concrete and the GFRP rebars.

Finite element modeling of the impact of heavy vehicles on highway and pedestrian bridge decks

Collisions of over-height vehicles, such as tipper trucks or exceptional convoys, with highway and pedestrian bridge decks can compromise the safety of road users and cause major economic losses due to road closures required by structural inspections and urgent repairs. Several studies proposed to reduce the probability of occurrence of such events by using steel pedestals, static road signs or impact detection systems. In spite of these preventive solutions, bridge decks are still frequently impacted by heavy vehicles as a result of the densification of transportation networks. In this study, a practical finite element approach is proposed to investigate the key parameters influencing the dynamic response of a bridge deck subjected to an impact from an excavator or other heavy construction equipment trailed on a flatbed truck. The developed finite element models are used to carry out a parametric study on vehicle-bridge systems with varying properties such as the lateral stiffness and mass of the bridge deck, and the mass and the velocity of the colliding vehicle. It is first shown that contact compliance is a critical factor that should be selected carefully after several numerical tests supported by engineering judgment. The obtained results confirm that dynamic effects are key factors to be taken into account when studying vehicle-bridge collisions. The trends characterizing the collisions between a heavy vehicle and the bridge deck are studied in terms of contact forces and their duration, the structural response of the bridge deck, as well as kinetic energy of the impacting vehicle. Some of the results obtained are compared to analytical predictions proposed in Eurocode.

Repair of Damaged Prestressed Concrete Girders with FRP and FRCM Composites

AbstractTraditional methods used to repair prestressed concrete (PC) girders subjected to impact damage due to overheight vehicles include strand splicing and external posttensioning. These methods...

Structural performance of impact damaged and repaired concrete bridge girder using GFRP rebars

Abstract Overpass bridge girders are susceptible to impact damage of over-height vehicles, creating a traffic hazard and structural deficiency. The repair for a damaged girder has to meet adequate criteria for the safety, repair time and economy. This paper presents a case study for the repair of such an impact damaged concrete girder on the Lyndon B. Johnson Express construction project, located on I-635 and I-35 freeways in Dallas, Texas. The impact caused concrete loss and exposed several prestressing strands on the exterior girder. The overpass had been completed while the old route was open below, causing a temporary vertical lower clearance than the final design, leading to the impact. The novel and innovative repair process involved fiber glass (GFRP) rebars, bonding epoxy and repair mortar. These rebars enhanced the flexural capacity of the repaired section and supplemented the mortar strength. Onsite load testing was employed to verify the performance of the repaired structure. Theoretical model of the composite girder before and after repair was employed. The strain data from the model compared well with the load testing data. The repair scheme drastically increased the stiffness of the damaged girder, resulting in about 50 % reduction in the bottom strains. The beneficial effect of the repair resulted in large increases in the net compressive stresses (200–300 %) at the girder bottom through the increase of the section stiffness and reduction of the gravity load stresses. Stresses remained well below the elastic range for concrete and the GFRP rebars.

Understanding the Problem of Bridge and Tunnel Strikes Caused by Over-height Vehicles

A bridge or tunnel strike is an incident in which a vehicle that is taller than the clearance underneath the structure (over-height), typically a lorry or double-decker bus, collides with the structure causing damage. This can lead to injuries, fatalities and/or, in worst case scenario, train derailments. Bridge and tunnel strikes are costly and expensive. The annual maintenance costs to repair and service the structure have been reported to range in the tens-to-hundreds of thousands (£) while the average cost per strike ranges between £5,000 to £25,000. In this paper, we present a comprehensive synthesis of the nature and scope of the problem of bridge and tunnel strikes, followed by the current state of practice and current state of research. Bridge and tunnel strikes still occur with high frequency, and prevention systems (passive, sacrificial and active) available on the market are often too expensive. Bridge-owners are seeking an affordable yet reliable system that is cheap enough for widespread installation without compromising the accuracy and performance of such a system.

Behavior of Laterally Damaged Prestressed Concrete Bridge Girders Repaired with CFRP Laminates Under Static and Fatigue Loading

Many bridges are subject to lateral damage for their girders due to impact by over-height vehicles collision. In this study, the optimum configurations of carbon fiber reinforced polymers (CFRP) laminates were investigated to repair the laterally damaged prestressed concrete (PS) bridge girders. Experimental and analytical investigations were conducted to study the flexural behavior of 13 half-scale AASHTO type II PS girders under both static and fatigue loading. Lateral impact damage due to vehicle collision was simulated by sawing through the concrete of the bottom flange and slicing through one of the prestressing strands. The damaged concrete was repaired and CFRP systems (longitudinal soffit laminates and evenly spaced transverse U-wraps) were applied to restore the original flexural capacity and mitigate debonding of soffit CFRP longitudinal laminates. In addition to the static load tests for ten girders, three more girders were tested under fatigue loading cycles to investigate the behavior under simulated traffic conditions. Measurements of the applied load, the deflection at five different locations, strains along the cross-section height at mid-span, and multiple strains longitudinally along the bottom soffit were recorded. The study investigated and recommended the proper CFRP repair design in terms of the CFRP longitudinal layers and U-wrapping spacing to obtain flexural capacity improvement and desired failure modes for the repaired girders. Test results showed that with proper detailing, CFRP systems can be designed to restore the lost flexural capacity, sustain the fatigue load cycles, and maintain the desired failure mode.