Structural Control and Health Monitoring

During the operation of commercial vehicles, drivers are usually exposed to long-term vibrations and acquire several health problems. Moreover, the end-stop impacts caused by large-magnitude vibrations or shocks may affect driving performance and result in injuries. A study of magnetorheological (MR) seat suspension controlled by a novel tuning control strategy is conducted in this research to reduce vibrations and avoid end-stop impacts. First, the MR damper’s characteristics are tested, and a mathematical model of MR seat suspension is established. Then, an improved tuning control strategy is designed based on this model. The proposed strategy has three control stages that can be adjusted according to the suspension stroke to improve seat comfort or avoid end-stop impacts. Each part of the control strategy is designed separately, and the vibration attenuation performance of this seat suspension is evaluated with a simulation for three excitations, i.e., harmonic excitation, bump excitation, and random road excitation. Finally, an experiment is conducted to verify the conclusion of the simulation. The seat suspension with the proposed control shows good performances on vibration attenuation and end-stop impact reduction. Compared with a passive seat, the vibration level is reduced by around 27% and end-stop impact is avoided when semiactive suspension with the proposed strategy is used. It also shows the best overall performance among the three experimental algorithms. Both the simulation and the experiment results indicate that the vibration attenuation performance of the seat suspension can be greatly improved with the improved tuning control strategy.

Structural health monitoring (SHM) can continuously and nondestructively evaluate the state and performance of structures using the structural responses to external loads or environmental conditions. Moreover, online or real-time SHM of civil structures provides significant advantages over periodic or manual inspection methods, especially under disaster loadings, where the consequences of failure can be severe. To achieve it, performing system identification and damage detection recursively, said recursive subspace identification (RSI), is a promising solution, and SHM based on the algorithms can evaluate damage or deterioration of civil structures, give insight into the health and performance of a structural system, and provide valuable information for decision-making on maintenance and repair. However, the time-consuming decompositions frustrate these algorithms. As a compromise, additional processing is required to implement online and real-time applications. This study demonstrates a modified algorithm that takes advantage of the projection approximation subspace tracking (PAST) algorithm and the repeated system matrices in the extended observability matrix. The modification can reduce numerical decompositions and improve important timeliness for online or real-time SHM of civil structures. Both the numerical simulation and experimental investigation have been used to verify the proposed method, and the results show its capability to determine the changes in the dynamic characteristics of a structure in either the laboratory experiment or in the field application. In the last place, the discussion and some conclusions are also drawn in this paper.

In this paper, a new fiber Bragg grating (FBG) strain sensor with adjustable sensitivity is invented. The sensitivity adjustment, strain sensing, and temperature compensation principles of the sensor and the corresponding formulae are developed. The prototype sensor specimen is developed, and a series of tests are performed to investigate its strain sensitivity and temperature compensation characteristics. The results show that the strain sensitivity of the sensor can be adjusted effectively by the correspondent L/L_FBG parameter, with an acceptable discrepancy within ±5% of the theoretical value. The linearity, repeatability, and hysteresis were analyzed, and the errors were 0.98%, 1.15%, and 0.09%, respectively, with excellent performance. When the temperature difference was 20°C, through temperature compensation calibration, the error between the monitored strain and the actual strain was within 5% after temperature compensation correction, showing that this new type of FBG strain sensor can meet the strain monitoring needs of various engineering structures and provide reliable data acquisition.

The λ-factors for stiffness and damping of rubber bearings should be experimentally assessed during the qualification process or deduced from tests performed on material specimens. Moreover, the λ-factors suggested in the informative annexes of EN 15129 and of EC8-part 2 can be also used as reference values. However, they are derived from outdated experimental campaigns and do not refer to all the sources of variability. In this paper, a statistical analysis on a significant set of rubber compounds, certified according to EN 15129 from different suppliers, is carried out to assess the current variability of the dynamic properties of such compounds. Different sources of variability may be identified by distinguishing between behavioural and environmental effects. For elastomeric bearings, especially high-damping rubber (HDR) ones, the main behavioural effects are strain amplitude, strain rate dependence, and cyclic degradation, whereas the environmental effects are due to temperature variation and ageing. All these sources of variability have been analysed in this paper. The results of the statistical analysis have been used to propose a new set of λ-factors for all the source of variability studied. Such new values have been compared with the ones suggested by the codes when available. The main inconsistencies found have been highlighted and commented in this paper. Finally, some considerations about the influence of such variability on the structural response of base-isolated structures have been drawn by focusing on both the isolation system and the superstructure.

Condition rating of bridges is specified in many countries since it provides a basis for the decision-making of maintenance actions such as repair, strengthening, or limitation of passing vehicle weight. In practice, professional engineers check the textual description of damages to bridge members, such as girders, bearings, expansion joints, and piers that are acquired from periodic inspections, and then make a rating of the bridge condition. The task is time-consuming and labor-intensive due to the large amount of detailed data buried in the inspection reports. In this paper, a natural language processing- (NLP-) based machine learning (ML) approach is proposed for automated and fast bridge condition rating, which can efficiently extract the information of deficiencies in bridge members. The proposed approach involves three major steps, say, data repository establishment, NLP-based textual data processing, and ML-based bridge condition rating prediction. The data repository is established with the inspection reports of 263 concrete bridges, and in total there, are four condition levels for the bridges. Then, the NLP-based textual data processing approach is implemented to calculate the word frequency and the word clouds to visualize the characteristics of bridges in different condition levels. Finally, four typical ML techniques are adopted to generate the predictive model of the bridge condition rating. The results indicate that the NLP-based ML prediction model has an accuracy of 89% and is very efficient so that it can be used for large-scale applications such as condition rating for regional-level bridges.

Vehicle load estimation and health monitoring of bridges are of great importance for the health monitoring of bridge structure under vehicle loads. Traditional methods for the estimation of vehicle load require the positions of the vehicles. The vehicle position tracking is generally conducted in offline manner and requires the installation of additional sensors. To resolve these problems, we developed a Bayesian probabilistic approach for the online estimation of vehicle loads, vehicle positions, and structural parameters for bridges. The crux is to model the vehicle load vector as a modulated filtered Gaussian white noise due to the fact that the vehicle-bridge interaction forces are in essence the responses of the vehicle-bridge coupled system under the excitation of the road roughness described by Gaussian random field and the constant vehicle weights. Furthermore, the vehicle speed vector is introduced to track the unknown positions of vehicles. There are three appealing features in this approach. First, it allows the simultaneous estimation of vehicle loads, vehicle positions, and structural parameters in an online manner. Second, this method allows for time-varying vehicle speed tracking. Third, the proposed method is applicable to the case with multiple vehicles. Examples for the case where single/multiple vehicles pass across bridges with uniform speeds/variable speeds are presented to demonstrate the feasibility of the proposed method for vehicle load estimation, vehicle position tracking, and bridge structural identification using only strain measurements.