A structural damage detection method based on parameter identification using an iterative neural network (NN) technique is proposed in this study. The NN model is first trained off-line using an initial training data set that consists of assumed structural parameters as outputs and their corresponding dynamic characteristics as inputs. The structural parameters are assumed with different levels of reduction to simulate various degrees of structural damage. The concept of orthogonal array is adopted to generate the representative combinations of parameter changes, which can significantly reduce the number of training data while maintaining the data completeness. A modified back-propagation learning algorithm is proposed which can overcome possible saturation of the sigmoid function and speed up the training process. The trained NN model is used to predict the structural parameters by feeding in measured dynamic characteristics. The predicted structural parameters are then used in the FE model to calculate the dynamic characteristics. The NN model would go through a retraining process if the calculated characteristics deviate from the measured ones. The identified structural parameters are then used to infer the location and the extent of structural damages. The proposed method is verified both numerically and experimentally using a clamped-clamped T beam. The results indicate that the current approach can identify both the location and the extent of damages in the beam.

Structural health monitoring based maintenance (SHMBM) is a basic engineering effort to collect maintenance information, forming a database system to open to the public or citizens for making decision on a suitable solution strategy to extend a structure's life. Sustainability of the infrastructure structural performance can be assessed by performing continuous structural health monitoring system (SHMS) on the structural deformational properties. The essence of SHMS can be considered to involve measurement, inspection, and assessment of in‐service structures on a continuous basis with minimum labor requirement. However, human memory limitation, job changes, imperfections and inability to provide a reliable monitoring system can lead to overly optimistic reports on structural health. Therefore, a sustainable SHMS which fulfill ‘AtoE’ characteristics, i.e. accuracy, benefit, comprehensiveness, durability and ease of operation, is necessary to be consider in designing a reliable long‐term SHMS. Generally, those characteristics are difficult to compare quantitatively. Specifically, some qualitatively compared sensory technologies will be reviewed in this paper by comparing the application of load cell, stress meter, strain gauge and EM (elastomagnetic) sensory technology. Furthermore, some innovative sensory technologies such as GPS‐based MMS (movement monitoring systems), PDMD (peak displacement memory devices) and FOS (fiber optic sensors), are introduced to monitor global structural movement, partial structural movement and local structural deformational properties at different scales of monitored objects. Copyright © 2005 John Wiley & Sons, Ltd.

An electromagnetic sensor was assessed as a possible instrument for nondestructive detection and monitoring of corrosion in structural carbon steels. In this study, the magnetic response of three structural carbon steel rods (AISI 1018, AISI 1045, and AISI 1045-High Mn), was evaluated in the as-received (uncorroded) and corroded conditions. Initially, the material was systematically machined out from each steel rod, followed by the magnetic evaluation of each specimen. Other set of metal rods were exposed to uniform corrosion and later examined by the electromagnetic sensor. Correlations have been established between the degree of mass loss and magnetic response of the test specimen. Based on the results, it can be said that the electromagnetic sensor has the potential to be used as a reliable nondestructive tool to detect corrosion at early stages based on the variation in magnetic properties. A metallurgical analysis of all test rods was also undertaken, which showed that microstructures have an important effect of the magnetic properties of the steels.

No abstract

There is a compelling desire by power generating plants to continue running existing stations and components for several more years, despite many of them have surpassed their design service life. The idea is to avoid premature retirement, on the basis of the so-called design life, because actual useful life could often be well in excess of the design life. This can most readily be achieved by utilizing nondestructive monitoring methods to monitor the degradation of the microstructure, either when a station is down for maintenance or preferably when it is under operation. This study evaluates the use of quasi static hysteresis measurements as a possible procedure to evaluate creep in a 410 martensitic stainless steel, a material utilized in power plant components. The creep rupture tests were conducted at stresses of 100 and 200 MPa, temperatures of 500°C and 620°C, and the times varied between 48 and 120 hours. Following the creep tests all specimens were evaluated magnetically and then metallurgically by optical and scanning electron microscopy, x-ray diffraction (XRD) and by energy dispersive spectroscopy (EDS). The microstructural changes were compared with the magnetization changes. It was determined that the changes in the hysteresis curves were clearly detectable and correlated with the creep-induced damage.