Monitoring of impact loads is a very important technique in the field of structural health monitoring (SHM). However, in most cases it is not possible to measure impact events directly, so they need to be reconstructed. Impact load reconstruction refers to the problem of estimating an input to a dynamic system when the system output and the impulse response function are usually known. Generally this leads to a so called ill-posed inverse problem. It is reasonable to use prior knowledge of the force in order to develop more suitable reconstruction strategies and to increase accuracy. An impact event is characterized by a short time duration and a spatial concentration. Moreover the force time history of an impact has a specific shape, which also can be taken into account. In this contribution these properties of the external force are employed to create a sample-force-dictionary and thus to transform the ill-posed problem into a sparse recovery task. The sparse solution is acquired by solving a minimization problem known as basis pursuit denoising (BPDN). The reconstruction approach shown here is capable to estimate simultaneously the magnitude of the impact and the impact location, with a minimum number of accelerometers. The possibility of reconstructing the impact based on a noisy output signal is first demonstrated with simulated measurements of a simple beam structure. Then an experimental investigation of a real beam is performed.

This paper presents a multi helical ultrasonic imaging approach for quantitative corrosion damage monitoring of cylindrical structures. The approach consists of two stages. First a multi helical ultrasonic imaging (MHUI) algorithm is used to provide qualitative images of the structure of interest. Then, an optimization problem is solved in order to obtain quantitative damage information, such as thickness map. Experimental tests are carried out on a steel pipe instrumented with six piezoelectric transducers to validate the proposed approach. Three thickness recesses are considered to simulate corrosion damage. The results show the efficiency of the proposed approach for quantifying corrosion location, area and remnant thickness.

This paper provides an overview on development of long-span bridges monitoring in Japan, with emphasis on monitoring strategies, types of monitoring system, and effective utilization of monitoring data. Because of severe environment condition such as high seismic activity and strong wind, bridge monitoring systems in Japan historically put more emphasis on structural evaluation against extreme events. Monitoring data were used to verify design assumptions, update specifications, and facilitate the efficacy of vibration control system. These were among the first objectives of instrumentation of long-span bridges in a framework of monitoring system in Japan. Later, monitoring systems were also utilized to evaluate structural performance under various environment and loading conditions, and to detect the possible structural deterioration over the age of structures. Monitoring systems are also employed as the basis of investigation and decision making for structural repair and/or retrofit when required. More recent interest has been to further extend application of monitoring to facilitate operation and maintenance, through rationalization of risk and asset management by utilizing monitoring data. The paper describes strategies and several examples of monitoring system and lessons learned from structural monitoring of long-span bridges in Japan.

Historically the UK has been a pioneer and early adopter of experimental investigation techniques on new and operation structures, a technology that would now be descried as 'structural health monitoring' (SHM), yet few of these investigations have been enduring or carried out on the long span or tall structures that feature in flagship SHM applications in the Far East.

There is a need for rapid and objective assessment of concrete bridge decks for maintenance decision making. Infrared Thermography (IRT) has great potential to identify deck delaminations more objectively than routine visual inspections or chain drag tests. In addition, it is possible to collect reliable data rapidly with appropriate IRT cameras attached to vehicles and the data are analyzed effectively. This research compares three infrared cameras with different specifications at different times and speeds for data collection, and explores several factors affecting the utilization of IRT in regards to subsurface damage detection in concrete structures, specifically when the IRT is utilized for high-speed bridge deck inspection at normal driving speeds. These results show that IRT can detect up to 2.54 cm delamination from the concrete surface at any time period. It is observed that nighttime would be the most suitable time frame with less false detections and interferences from the sunlight and less adverse effect due to direct sunlight, making more "noise" for the IRT results. This study also revealed two important factors of camera specifications for high-speed inspection by IRT as shorter integration time and higher pixel resolution.

As the study of internal corrosion of pipeline need a large number of experiments as well as long time, so there is a need for new computational technique to expand the spectrum of the results and to save time. The present work represents a new non-destructive evaluation (NDE) technique for detecting the internal corrosion inside pipeline by evaluating the dielectric properties of steel pipe at room temperature by using electrical capacitance sensor (ECS), then predict the effect of pipeline environment temperature (${\theta}$) on the corrosion rates by designing an efficient artificial neural network (ANN) architecture. ECS consists of number of electrodes mounted on the outer surface of pipeline, the sensor shape, electrode configuration, and the number of electrodes that comprise three key elements of two dimensional capacitance sensors are illustrated. The variation in the dielectric signatures was employed to design electrical capacitance sensor (ECS) with high sensitivity to detect such defects. The rules of 24-electrode sensor parameters such as capacitance, capacitance change, and change rate of capacitance are discussed by ANSYS and MATLAB, which are combined to simulate sensor characteristic. A feed-forward neural network (FFNN) structure are applied, trained and tested to predict the finite element (FE) results of corrosion rates under room temperature, and then used the trained FFNN to predict corrosion rates at different temperature using MATLAB neural network toolbox. The FE results are in excellent agreement with an FFNN results, thus validating the accuracy and reliability of the proposed technique and leads to better understanding of the corrosion mechanism under different pipeline environmental temperature.