In this paper, we suggest and analyze a three-step iterative scheme for asymptotically nonexpansive mappings in Banach spaces. The new iterative scheme includes Ishikawa-type and Mann-type interations as special cases. The results obtained in this paper represent an extension as well as refinement of previous known results.  2002 Elsevier Science (USA)

No abstract

No abstract

No abstract

In this paper, based on the concept of decentralized information structures and artificial neural networks, a decentralized parametric identification method for damage detection of structures with multi-degreesof-freedom (MDOF) is conducted. First, a decentralized approach is presented for damage detection of substructures of an MDOF structure system by using neural networks. The displacement and velocity measurements from a substructure of a healthy structure system and the restoring force corresponding to this substructure are used to train the decentralized detection neural networks for the purpose of identifying the corresponding substructure. By using the trained decentralized detection neural networks, the difference of the interstory restoring force between the damaged substructures and the undamaged substructures can be calculated. An evaluation index, that is, relative root mean square (RRMS) error, is presented to evaluate the condition of each substructure for the purpose of health monitoring. Although neural networks have been widely used for nonparametric identification, in this paper, the decentralized parametric evaluation neural networks for substructures are trained for parametric identification. Based on the trained decentralized parametric evaluation neural networks and the RRMS error of substructures, the structural parameter of stiffness of each subsystem can be forecast with high accuracy. The effectiveness of the decentralized parametric identification is evaluated through numerical simulations. It is shown that the decentralized parametric evaluation method has the potential of being a practical tool for a damage detection methodology applied to structure-unknown smart civil structures.

Most of the currently available vibrationbased damage detection approaches identify structural damage in the form of decrease in structural stiff

A structural parameter identification and damage detection approach using displacement measurement time series is proposed, and the performance of the approach is validated experimentally with a frame structure model in a healthy condition and with joint connection damages. The dynamic displacement response of the frame structure under base excitation is measured by noncontact laser displacement sensors. The proposed approach is carried out using two neural networks: one is called displacement-based neural network emulator (DNNE) and the other is called parametric evaluation neural network (PENN). First, the theoretical basis and the selection of the input and output of the DNNE and the PENN are explained, and second, an identification index called root mean square (RMS) of prediction displacement difference vector (RMSPDDV) is defined. The performance of the proposed methodology for damage detection of the frame structure model with different joint damage scenarios introduced by loosening the bolts connecting the beams and columns is investigated with the direct use of displacement measurement under base excitations. The results from the proposed time domain displacement-based identification approach are compared with them based on the extracted frequencies and show that the proposed time domain methodology can identify the variation of interstory stiffness due to the joint damage with acceptable accuracy without any modal shapes and frequencies extracted from a dynamic test. The proposed approach provides an alternative way for damage detection of engineering structures by the direct use of structural dynamic displacement measurements.

No abstract