This paper reviews a range of statistical approaches to illustrate the influence of data quality and quantity on the probabilistic modelling of traffic load effects. It also aims to demonstrate the importance of long-run simulations in calculating characteristic traffic load effects. The popular methods of Peaks Over Threshold and Generalized Extreme Value are considered but also other methods including the Box-Cox approach, fitting to a Normal distribution and the Rice formula. For these five methods, curves are fitted to the tails of the daily maximum data.Bayesian Updating and Predictive Likelihood are also assessed, which require the entire data for fittings. The accuracy of each method in calculating 75-year characteristic values and probability of failure, using different quantities of data, is assessed. The nature of the problem is first introduced by a simple numerical example with a known theoretical answer. It is then extended to more realistic problems, where long-run simulations are used to provide benchmark results, against which each method is compared. Increasing the number of data in the sample results in higher accuracy of approximations but it is not able to completely eliminate the uncertainty associated with the extrapolation. Results also show that the accuracy of estimations of characteristic value and probabilities of failure are more a function of data quality than extrapolation technique. This highlights the importance of long-run simulations as a means of reducing the errors associated with the extrapolation process.

Bridge weigh-in-motion (B-WIM) is a method by which the axle weights of a vehicle travelling at full highway speed can be determined using a bridge instrumented with sensors. Since the sensors are attached to the underside of a bridge, the instrumentation can be installed without disruption to traffic. This paper looks at the history of B-WIM, beginning with early work on weigh-in-motion technologies in the 1960's leading to its invention by Fred Moses and George Goble in the United States in the mid 1970's. Particular attention is devoted to Moses' original algorithm, which has been used by many systems since 1979 and is still utilized today by commercial developers of B-WIM systems. Research initiatives in Australia and Europe over the past 15 years have focused on improving B-WIM accuracy either by improving Moses' original algorithm or by developing new methods. The moving force identification (MFI) method models the dynamic fluctuation of axle forces on the bridge and holds particular promise. B-WIM accuracy depends on bridge site conditions as well as the particular data processing algorithm. The accuracy classifications of several B-WIM installations reported in the literature are summarized in this paper. Current accuracy levels are sufficient for selecting vehicles to be weighed using static scales, but insufficient for direct enforcement.

Abstract-The recent expansion of pervasive computing technology has contributed with novel means to pursue human activities in urban space. The urban dynamics unveiled by these means generate an enormous amount of data. These data are mainly endowed by portable and radio-frequency devices, transportation systems, video surveillance, satellites, unmanned aerial vehicles, and social networking services. This has opened a new avenue of opportunities, to understand and predict urban dynamics in detail, and plan various real-time services and applications in response to that. Over the last decade, certain aspects of the crowd, e.g. mobility, sentimental, size estimation and behavioral, have been analyzed in detail and the outcomes have been reported. This article mainly conducted an extensive survey on various data sources used for different urban applications, the state-of-the-art on urban data generation techniques and associated processing methods in order to demonstrate their merits and capabilities. Then, a possible crowd event detection framework is discussed which fuses data from all the available pervasive technology sources. In addition, available open-access crowd datasets for urban event detection are provided along with relevant Application Programming Interfaces, and finally, some open challenges and promising research directions are outlined.Index Terms-Urban sensing, pervasive technology, crowd mobility and management, information fusion, decision support system, benchmark datasets.

In this study a Structural Health Monitoring (SHM) system is combined with Bridge Weigh-inMotion (B-WIM) measurements of the actual traffic loading on a bridge to carry out a fatigue damage calculation. The SHM system uses the 'Virtual Monitoring' concept, where all parts of the bridge that are not monitored directly using sensors, are 'virtually' monitored using the load information and a calibrated Finite Element (FE) model of the bridge. Besides providing the actual traffic loading on the bridge, the measurements are used to calibrate the SHM system and to update the FE model of the bridge. The newly developed Virtual Monitoring concept then uses the calibrated FE model of the bridge to calculate stress ranges and hence to monitor fatigue at locations on the bridge not directly monitored. The combination of a validated numerical model of the bridge with the actual site-specific traffic loading allows a more accurate prediction of the cumulative fatigue damage at the time of measurement and facilitates studies on the implications of traffic growth. In order to test the accuracy of the Virtual Monitoring system, a steel bridge with a cable-stayed span in the Netherlands was used for testing.

With the growing number of well-aged bridges and the urgency in developing reliable, (pseudo-) real-time monitoring of structural safety and integrity, there is a worldwide and widespread campaign toward transforming structural health monitoring practice. Among these attempts, the application of data-driven approaches in developing damage identification techniques has received particular attention in recent years. Given the growing volume of structural health monitoring data, the power of data-driven approaches has been further exploited. These efforts have been predominantly focused on building and training algorithms using direct measurements from bridges. Although recent years have seen transformative technologies in producing cheap and wireless sensors, network-wide bridge instrumentation is logistically difficult and expensive. This has led to a new group of structural health monitoring systems entitled indirect or drive-by approaches. In drive-by systems, measurements from an instrumented vehicle are used to extract structural damage signatures. In other words, in these systems, the instrumented vehicle acts as both actuator and receiver while passing over a bridge. The main challenge in deploying drive-by approaches for damage identification purposes is that the signals collected on drive-by vehicles also embody signatures from the vehicle, road/rail profile and are easily contaminated by environmental and operational conditions. Furthermore, the majority of current drive-by damage identification systems rely on prior knowledge of vehicle or bridge dynamic characteristics which has led to limited application of the concept in practice so far. To address these challenges, this study employs a powerful class of deep learning algorithm to develop a damage identification system using measurements on an instrumented travelling train. The proposed algorithm is capable of automatically extracting damage signatures from train-borne measurements only. To demonstrate the algorithm’s capability, the method is applied to measurements collected on a model instrumented train travelling on a simply supported model steel bridge. For this purpose, a deep convolutional neural network is built, optimised, trained and tested to detect damage using acceleration signals collected on the instrumented train only. The hyperparameters of the algorithm are optimised using the Bayesian optimisation technique. The accuracy of the algorithm is experimentally tested for four positive damage scenarios (combination of two different locations and intensity) and three different travelling speeds. This is the first demonstration of the data-driven drive-by damage identification system under scaled operational environment conditions. The performance of the proposed method is discussed under different travelling speeds and different damage states. The result shows that the proposed method can accurately and automatically detect and classify damage under varying speed, rail irregularities and operational noise using train-borne measurements only and offers a great promise in transforming the future of bridge damage identification system.