Integrated Vehicle And Road Information Acquisition Based On IoT Monitoring Of Pavement Vibration

Pavement dynamic responses under vehicle loading provide traffic and service condition information that is important to traffic control and road maintenance decision making.Conventional monitoring systems for pavement dynamic responses are inefficient due to their high power consumptions,high costs and nonlinear relationships among various factors.It is difficult to deploy these systems widely.To overcome these limitations,this research focuses on the traffic-induced vibrations monitoring and its application to asphalt pavement by using the technology of Internet of Things(IoT).The scope of this dissertation research includes theoretical analysis,finite element simulation,laboratory test and field test.The main research activities and discoveries are described and presented as follows:An acceleration sensing node for pavement vibration monitoring was developed.The node component selection,hardware design,and node encapsulation were accomplished.This sensing node is an intelligent sensing unit with high integration.It not only has sensing capabilities,but also can process,store and transmit sensing data.The service performance of the sensing node was evaluated by waterproof test,compression test,and sensing performance analysis.The results demonstrate that the sensing node is high in strength,waterproof,and sensitivity and resolution.These characteristics enable its use in practical road environments.Then,the sensing nodes thus designed and fabricated were installed on a road side to collect the pavement vibration signals under the loading of three types of vehicles.New algorithms for computing traffic flow,speed,and axle length were developed.Finally,the effects of vehicle speed,vehicle weight,and location of vehicle load on the vibration amplitude and the accuracy of the monitoring results were statistically evaluated.A data-driven method to process the vibration data to abstract comprehenisve traffic information was proposed.A large amount of raw data was collected in real time by using a pavement vibration monitoring system according to the design on a real road.This system includes acceleration sensing nodes,gateway and data acquisition interface.The data was processed with an efficient algorithm to compute the vehicle speed,distance between axles,driving direction,location of a vehicle and the traffic volume.Vehicle speed and the distance between axles were back-calculated from monitored information and verified with actual measurements.A three-layer artificial neural network was developed to classify the vehicle types.The video validation shows that it is difficult for the embedded monitoring system to distinguish some vehicles in type 1,type2 and type3.But the system can classify the vehicles in other types effectively.A k-means++ cluster analysis was developed and used to find out the abnormal vehicle weight.This improves the efficiency of the vehicle classification and control.The pavement dynamic responses under random nonuniform moving loads were simulated and the signals of pavement vibration responses were analyzed using a variety of situations.A quarter vehicle model was developed to obtain the random dynamic loads.The impacts of vehicle weight,vehicle speed and road roughness on vehicle dynamic loads were analyzed.Considering the load spatial distribution and randomness,a random non-uniform moving load was applied to a three-dimensional finite element pavement model by the developing the DLOAD subroutine.Thus,the stress-strain and vibration response of pavement were obtained.Numerical analyses showed that the damage of the surface layer due to the vehicle load would be underestimated without considering the random and spatial characteristics of the loads.The vibration modes changed considerably for the different roadway service conditions.The characteristics of vibration w aveform reflect the level of road roughness,the stiffness of the pavement materials,and the integrity of pavement structure.The vibration amplitude,the time-domain signal waveform,the frequency distribution,and the vibration energy can be potential indexes for evaluating roadway service condition.A prototype pavement vibration monitoring system based on IoT was built,which consists of self-powered front-end devices and back-end devices.The front-end devices include sensing nodes and a gateway.The back-end devices include remote servers and browsers.In order to achieve the long-term wireless monitoring,the remote servers should communicate with the gateways through 4G,and low-power communication protocol LoRaWan and a "sleep-wake" mechanism to reduce the power consumption needed for the communication between the gateway and the sensing nodes.The communication test between the gateway and the sensing node indicates that the radius of the Lora communication range is at least 500m.The deployment of sensing nodes were optimized by simulation from the perspective of energy consumption.Simulation results show that when using LoRa point-to-point communication,a gateway can be set up within a radius of 105m to balance the energy consumption of the overall network.In addition,the retransmission mechanism and time synchronization mechanism were discussed.The prototype monitoring platform including a data layer,a logic layer,a presentation layer and a client layer was designed.It adopted the B/S mode and the Model-View-Controller architecture.In brief,this study has developed a set of practical and integrated technology for monitoring pavement vibration under moving vehicle loads.These technologies can monitor pavement vibration response that can be used in intelligent traffic control and road maintance decision making.In the future,a large number of sensing nodes can be deployed in the "smart road" to form a wide range of wireless sensor networks.The information of road health,traffic,and environment can be acquired so as to realize intelligent traffic control,scientific maintenance decision making and early-warming.