Abstract

The fuzzy set theory offers a bridge between the symbolic and numerical processing, allowing managing qualitative concepts useful in the decision-making process related to the seismic risk management and, in general, to the disaster risk management. Its use in the seismic risk evaluation is necessary in the cases where the data required to apply a conventional method of assessing risk are not available or are insufficient. One possible solution, considered in this article, is to replace the missing information by expert opinions and to process the resulting qualitative variables and linguistic qualifications instead of numerical values. This process is based on the fuzzy set theory. In order to achieve an effective management, the risk must be defined as the potential physical, economic, social and environmental consequences which occur due to hazards in a given period of time. From this holistic perspective and using the fuzzy set theory, the proposed numerical method calculates a level of the physical risk and level of the aggravating conditions related to social fragility and to the lack of resilience, to determine a total risk level. In the article are included two examples of application of the proposed method and the obtained results are compared with those corresponding to a conventional method of holistic evaluation.

Abstract

Intelligent Transportation Systems emerged to meet the increasing demand for more efficient, reliable and safer transportation systems. These systems combine electronic, communication and information technologies with traffic engineering to respond to the former challenges. The benefits of Intelligent Transportation Systems have been extensively proved in many different facets of transport and Soft Computing has played a major role in achieving these successful results. This book chapter aims at gathering and discussing some of the most relevant and recent advances of the application of Soft Computing in four important areas of Intelligent Transportation Systems as autonomous driving, traffic state prediction, vehicle route planning and vehicular ad hoc networks. Document type: Part of book or chapter of book

Abstract

Class imbalance is among the most persistent complications which may confront the traditional supervised learning task in real-world applications. Among the different kind of classification problems that have been studied in the literature, the imbalanced ones, particularly those that represents real-world problems, have attracted the interest of many researchers in recent years. In order to face this problems, different approaches have been used or proposed in the literature, between then, soft computing and ensemble techniques. In this work, ensembles and fuzzy techniques have been applied to real-world traffic datasets in order to study their performance in imbalanced real-world scenarios. KEEL platform is used to carried out this study. The results show that different ensemble techniques obtain the best results in the proposed datasets. Document type: Part of book or chapter of book

Abstract

Transportation systems play a fundamental role in nowadays society. Indeed, every developed or countries undergoing development have invested and keep investing to build a safe and functional transportation network. The main concern nowadays, particularly for developed countries that already have a very complete network, is to keep it operational under all conditions. However, due to the network extension and increased budget constraints, such task is difficult to accomplish. In the framework of transportations networks, particularly for railway, slopes are perhaps the element for which their failure can have a strongest impact at several levels. Although there are some models and systems to detect slope failures, most of them were developed for natural slopes, presenting some constrains when applied to engineered (human-made) slopes. They have limited applicability as most of the existing systems were developed based on particular case studies or using small databases. Moreover, another aspect that can limit its applicability is related with the information used to feed them, such as data taken from complex tests or from expensive monitoring systems. Aiming to overcome this drawback, we took advantage of the high flexible learning capabilities of Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs), which have been used in the past to model complex nonlinear mappings. Both data mining algorithms were applied in the development of a classification tool able to identify the stability condition of a rock cutting slope, keeping in mind the use of information usually collected during routine inspections activities (visual information) to feed them. For that, two different strategies were followed: nominal classification and regression. Moreover, to overcome the problem of imbalanced data, three training sampling approaches were explored: no resampling, SMOTE and Oversampling. The achieved results are present and discussed, comparing the performance of both algorithms (ANN and SVM) according to each modeling strategy as well as the effect of the sampling approaches. This work was supported by FCT – “Fundação para a Ciência e a Tecnologia", within ISISE, project UID/ECI/04029/2013 as well Project Scope: UID/CEC/00319/2013 and through the post-doctoral Grant fellowship with reference SFRH/BPD/94792/2013. This work was also partly financed by FEDER funds through the Competitivity Factors Operational Programme - COMPETE and by national funds through FCT within the scope of the project POCI-01-0145-FEDER-007633. This work has been also supported by COMPETE: POCI01-0145-FEDER-007043. A special thanks goes to Network Rail that kindly make available the data (basic earthworks examination data and the Earthworks Hazard Condition scores) used in this work. info:eu-repo/semantics/publishedVersion