Design and Preliminary Testing of Demand-Responsive Transverse Rumble Strips

Transverse rumble strips are common practice to alert drivers by engaging their auditory and tactile senses in addition to visual senses by traffic signals. However, continuous exposure to noise and vibration by transverse rumble strips often results in diminished effectiveness and erratic behaviors, leading to additional safety challenges. In response, demand-responsive transverse rumble strips were developed as traffic safety countermeasures that reduce unnecessary noise and vibration associated with transverse rumble strips by incorporating active control of the rumble strips. Rather than staying static, demand-responsive transverse rumble strips are activated based on the presence of pedestrians, at predesignated times, or in response to abrupt changes in traffic flow. To evaluate the effectiveness of demand-responsive transverse rumble strips, the research team assessed noise and vibration data, both inside the vehicles and on the roadside, for various types of vehicles traveling at different speeds. The test data indicate that demand-responsive transverse rumble strips produced noticeable in-vehicle noise and vibration that could alert drivers to downstream events. Furthermore, demand-responsive transverse rumble strips generated sufficient noise to alert roadside pedestrians to vehicle presence but at low enough level to be considered as acceptable for a residential neighborhood use. Accordingly, demand-responsive transverse rumble strips could address the challenges that static transverse rumble strips face, by providing a design with relatively limited noise while enhancing safety

Dynamic leveling control of a wireless self-balancing ROV using fuzzy logic controller

A remotely operated vehicle (ROV) is essentially an underwater mobile robot that is controlled and powered by an operator outside of the robot working environment. Like any other marine vehicle, ROV has to be designed to float in the water where its mass is supported by the buoyancy forces due to the displacement of water by its hull. Vertically positioning a mini ROV in centimetres resolution underwater and maintaining that state requires a distinctive technique partly because of the pressure and buoyancy exerted by the water towards the hull and partly because of the random waves produced by the water itself. That being said, the aim of the project is to design and develop a wireless self-balancing buoyancy system of a mini ROV using fuzzy logic controller. A liquid level sensor has been implemented to provide feedback to the Arduino microcontroller. A user-friendly graphical user interface (GUI) has been developed for real-time data monitoring as well as controlling the vertical position of the ROV. At the end of the project, the implemented fuzzy control system shows enhanced and better performance when compared with one without a controller, a proportional-derivative (PD) controller, and a proportionalintegral-derivative (PID) controller

Automated Measurement of Heavy Equipment Greenhouse Gas Emission: The case of Road/Bridge Construction and Maintenance

Road/bridge construction and maintenance projects are major contributors to greenhouse gas (GHG) emissions such as carbon dioxide (CO2), mainly due to extensive use of heavy-duty diesel construction equipment and large-scale earthworks and earthmoving operations. Heavy equipment is a costly resource and its underutilization could result in significant budget overruns. A practical way to cut emissions is to reduce the time equipment spends doing non-value-added activities and/or idling. Recent research into the monitoring of automated equipment using sensors and Internet-of-Things (IoT) frameworks have leveraged machine learning algorithms to predict the behavior of tracked entities. In this project, end-to-end deep learning models were developed that can learn to accurately classify the activities of construction equipment based on vibration patterns picked up by accelerometers attached to the equipment. Data was collected from two types of real-world construction equipment, both used extensively in road/bridge construction and maintenance projects: excavators and vibratory rollers. The validation accuracies of the developed models were tested of three different deep learning models: a baseline convolutional neural network (CNN); a hybrid convolutional and recurrent long shortterm memory neural network (LSTM); and a temporal convolutional network (TCN). Results indicated that the TCN model had the best performance, the LSTM model had the second-best performance, and the CNN model had the worst performance. The TCN model had over 83% validation accuracy in recognizing activities. Using deep learning methodologies can significantly increase emission estimation accuracy for heavy equipment and help decision-makers to reliably evaluate the environmental impact of heavy civil and infrastructure projects. Reducing the carbon footprint and fuel use of heavy equipment in road/bridge projects have direct and indirect impacts on health and the economy. Public infrastructure projects can leverage the proposed system to reduce the environmental cost of infrastructure project

Densely distributed and real-time scour hole monitoring using piezoelectric rod sensors

This study aims to validate a piezoelectric driven-rod scour monitoring system that can sense changes in scour depth along the entire rod at its instrumented location. The proposed sensor is a polymeric slender rod with a thin strip of polyvinylidene fluoride that runs through its midline. Extraction of the fundamental frequency allows the direct calculation of the exposed length (or scour depth) of the slender rod undergoing fluid flow excitation. First, laboratory validation in dry conditions is presented. Second, hydrodynamic testing of the sensor system in a soil-bed flume is discussed. Each rod was installed using a three-dimensional-printed footing designed for ease of installation and stabilization during testing. The sensors were installed in a layout designed to capture symmetric scour conditions around a scaled pier. In order to analyze the system out of steady-state conditions, water velocity was increased in stages during testing to induce different degrees of scour. As ambient water flow excited the portion of the exposed rods, the embedded piezoelectric element outputted a time-varying voltage signal. Different methods were then employed to extract the fundamental frequency of each rod, and the results were compared. Further testing was also performed to characterize the relationship between frequency outputs and flow velocity, which were previously thought to be independent. In general, the proposed driven-rod scour monitoring system successfully captured changing frequencies under varied flow conditions

Framework for pedestrian walking behaviour recognition to minimize road accident

Pedestrian walking misbehaviour represents a severe problem to road safety. Therefore, pedestrian behaviour classification is a perfect solution in providing safety for both pedestrians and vehicles by exchanging movement information among entities via wireless communication. However, wireless communication has critical issues with network failure, and these issues significantly affect the communication system. Thus, the framework involved two modules for pedestrian walking behaviour classification in a vehicle-to-pedestrian (V2P) context is proposed. In the methodology, this study discloses five useful stages. Firstly, mobile phone users' irregular walking behaviour is investigated using a questionnaire to determine their options on mobile usage in the street. Secondly, four different testing scenarios are chosen to acquire pedestrian walking data using the gyroscope sensor, where the essential features were extracted and selected. Thirdly, the pedestrian's behaviour is recognized using grid optimizer in machine learning. Fourthly, four standard vectors for pedestrian walking behaviour are developed. Fifthly, the performance of the proposed classification methods is validated and evaluated against multiple scenarios and features. Two sets of real-time data are presented in this work. The first one is related to the questionnaire data, consisting of 262 respondent samples, while the second set has 263 samples of pedestrian walking signals. The results indicate the following: (1) From 262 samples, 66.80% and 48.10% of respondents use mobile phones for calling and chatting, respectively. (2) 263 samples of participants are obtained and analysed, and 90 features are extracted from each sample. (3) 100% classification accuracy are obtained for each class (normal walking, calling, chatting, and running) using the grid optimiser method in machine learning. (4) The precision of classification using Euclidean algorithm for normal walking and calling is 70%. In contrast, for chatting and running behaviour, the accuracy is 100% and 80%, respectively. This study's implication serves the safety system in the V2P context by programming the proposed framework as an application in smartphones for exchanging pedestrian information to the vehicles for avoiding accidents

Structural health monitoring and bridge condition assessment

Thesis (Ph.D.) University of Alaska Fairbanks, 2016This research is mainly in the field of structural identification and model calibration, optimal sensor placement, and structural health monitoring application for large-scale structures. The ultimate goal of this study is to identify the structure behavior and evaluate the health condition by using structural health monitoring system. To achieve this goal, this research firstly established two fiber optic structural health monitoring systems for a two-span truss bridge and a five-span steel girder bridge. Secondly, this research examined the empirical mode decomposition (EMD) method’s application by using the portable accelerometer system for a long steel girder bridge, and identified the accelerometer number requirements for comprehensively record bridge modal frequencies and damping. Thirdly, it developed a multi-direction model updating method which can update the bridge model by using static and dynamic measurement. Finally, this research studied the optimal static strain sensor placement and established a new method for model parameter identification and damage detection.Chapter 1: Introduction -- Chapter 2: Structural Health Monitoring of the Klehini River Bridge -- Chapter 3: Ambient Loading and Modal Parameters for the Chulitna River Bridge -- Chapter 4: Multi-direction Bridge Model Updating using Static and Dynamic Measurement -- Chapter 5: Optimal Static Strain Sensor Placement for Bridge Model Parameter Identification by using Numerical Optimization Method -- Chapter 6: Conclusions and Future Work

The ISO standard: Guide for the evaluation of human exposure to whole-body vibration

The international guideline is discussed in terms of safety and human tolerance. Charts for equal subjective vibration intensity, subjective judgement of equal fatigue, and severe discomfort boundaries are included

Early Detection of Near-Surface Void Defects in Concrete Pavement Using Drone Based Thermography and GPR Methods

The goal of this research is to evaluate the feasibility and the performance of using UAV-mounted infrared thermography (IRT) and ground penetration radar (GPR) to detect sub-surface voids caused by consolidation issues in concrete pavement. The motivation of the study is to identify the consolidation defects as early as the initial set of concrete to avoid having this problem in large pavement sections, which is costly and time consuming to repair. Using the two technologies in combination to detect subsurface voids in the concrete initial set stage is new and aims to take advantage of the strengths and minimize the limitations of each method. UAV-based IRT can cover large areas of the pavements in a short amount of time, while GPR can provide higher accuracy in locating the defects horizontally and vertically. Therefore, the combination of the two technologies can allow detection of small voids in large areas with improved confidence. In this project, both laboratory and field tests were conducted with both methods, and coring samples were used for validation of results. The results from multiple specimens and multiple experiments suggested that both technologies performed well in detecting the subsurface voids in the concrete pavement’s initial set stage. Despite some limitations discussed in the report, the outcomes of the project provided evidence that these technologies can be used separately or together on the field as efficient and economical quality control tools in concrete pavement construction

Measuring cycle riding comfort in Southampton using an instrumented bicycle

The increased environmental awareness and the rising fuel costs make bicycles a more and more attractive mode of travel for short journeys. Considering the future prospect of this mode of transportation and the great advantages that it offers in terms of space consumption, health and environmental sustainability, several city authorities worldwide are presently undertaking schemes aiming at improving cycling infrastructure. The aim of the present study is to monitor the impact of such schemes on the riding comfort of cyclists, as expressed by the, usually lower, quantity and magnitude of vibrations occurring as a result of cycling over pavement defects. Millbrook Road East in the western edge of the city center of Southampton is used as a case study, where vibration measurements are taken by means of an instrumented bicycle during periods before and after a redevelopment scheme involving the resurfacing of the road pavement. The results show a clear overall improvement in cycling comfort post-redevelopment, with statistically significant reductions in both the number of high severity vibrations and of their magnitude in "typical" cycling trips taken on the road. However, instances of finishing "snags" in some parts of the surface appear to introduce new minor defects (e.g. around manholes) that are not visible to the naked eye, and these still have some negative effect on the riding experience. Moreover, the study highlights the detrimental impact that widespread pavement defects can have on riding comfort, which affect cyclists of all ages, abilities and styles