The interest in the prediction of corporate bankruptcy is increasing due to the implications associated with this phenomenon (e.g. economic, and social) for investors, creditors, competitors, government, although this is a classical problem in the financial literature.Two kinds of models are generally adopted for bankruptcy prediction: (i) accounting ratios based models and (ii) market based models. In the former, classical statistical techniques such as discriminant analysis or logistic regression models have been used, while in the latter the Moody's KMV model was adopted.This paper follows the first approach (i), and it is based on the analysis of the evolution of several financial indicators during a three-year period. A framework was developed, encompassing a total of 16 models. These differ in the data mining algorithm (e.g. Artificial Neural Networks or Decision Trees), the data used (all three years or just the last one) and the input attributes adopted (e.g. all accounting ratios or just the most significant ones). The experiments were conducted using the new Business Intelligence Development Studio of the Microsoft SQL Server. Very good results were achieved, with performances between 86% and 99% for all 16 models.

There is an increasing need of deploying automatic real-time decision support systems for civil engineering structures, making use of prediction models based in Artificial Intelligence techniques (e.g., Artificial Neural Networks) to support the monitoring and prediction activities. Past experiments with Data Mining (DM) techniques and tools opened room for the development of such a real-time Decision Support System. However, it is necessary to test this approach in a real environment, using real-time sensors monitoring. This study presents the development of prediction models for structures behavior and a novel architecture for operating in a real-time system.

Bridges are one of the primary infrastructures in our society. During the life cycle of a bridge structure, the service conditions should be evaluated on a regular basis in order to assure the necessary levels of strength and durability. Taking into account (i) the social-economic importance of bridges' use, (ii) the necessary safety assurance and (iii) the high costs of any physical intervention, there is a need for continuous online bridge monitoring, for investment and use optimization. Recently, smart structures, which combine remote sensors (which send a stream of time series data) with intelligent information systems for real-time decision support through embedded Data Mining (DM) models, have been proposed to handle this task. Indeed, the application of DM techniques to analyse civil engineering data has gained an increasing interest in recent years, due to intrinsic characteristics such as the ability to deal with nonlinear relationships.following ratios: global efficiency, structural adequacy and safety, serviceability, essentiality for public use, and special reductions, using a ratio-based framework, and data collected during inspections of bridges in the north of Portugal. In particular, the global efficiency ratio is very useful to identify intervention priorities and to schedule the repair, strengthening and rehabilitation needs.The obtained results are encouraging and the most accurate model for global efficiency presents a low error (Root Mean Squared Error of 0.149).This approach opens room for the development of intelligent decision support systems for Bridge Management Systems. These systems are being recognized as a good way to systematize all the management process and to minimize the ratio cost/benefit during the bridge lifetime.

Steel plate girder structures, composed of nominally flat plates connected together usually by welding, are used in large structures of vital importance for society. Plate girders are usually designed when hot rolled beams are not economic or show insufficient strength for design purposes. Due the postbuckling strength, and depending upon the geometry, the web panel is capable of carrying additional loads considerably in excess of that at which the web starts to buckle. Taking advantage of this postbuckling strength, a plate girder of high strength weight ratio can be designed. These plate girders can be designed as tapered plate girders, usually done by means of a web panel whose depth varies linearly. Designed in accordance with the distribution of bending moments, along the longitudinal direction of the structural systems, tapered web panels with variable inertia provide the required resistance. In this sense, the use of tapered plate girders is frequently a solution in cases of high moment variation, inducing to a rational and efficient solution (Zárate and Mirambell 2002). As a result of spectacular bridge failures in Europe, during the 1970s, the design of plated structures attracted great interest. However, the limitations of the favourable behaviour of plates were not known. This lack of knowledge led to large-scale research projects including experiments and theoretical development. The research in the actual area of nonlinear analysis of plated structures is sustained in the power of computers and their availability to researchers. Since 1930s that had been known that thin plates had a substantial post-critical resistance. Design was based on allowable stresses for plate buckling and the critical stress was the starting point. Design codes started to take the post-critical resistance into account by reducing the safety factors for plate buckling. Nowadays, the insecurity on modelling in the field of classical mechanics of materials can be considered almost irreducible. In the same sense, mechanical properties of the traditional