The non-destructive evaluation (NDE) of civil infrastructure has been an active area of research in recent decades. The traditional inspection of civil infrastructure mostly relies on visual inspection using human inspectors. To facilitate this process, different sensors for data collection and techniques for data analyses have been used to effectively carry out this task in an automated fashion. This review-based study will examine some of the recent developments in the field of autonomous robotic platforms for NDE and the structural health monitoring (SHM) of bridges. Some of the salient features of this review-based study will be discussed in the light of the existing surveys and reviews that have been published in the recent past, which will enable the clarification regarding the novelty of the present review-based study. The review methodology will be discussed in sufficient depth, which will provide insights regarding some of the primary aspects of the review methodology followed by this review-based study. In order to provide an in-depth examination of the state-of-the-art, the current research will examine the three major research streams. The first stream relates to technological robotic platforms developed for NDE of bridges. The second stream of literature examines myriad sensors used for the development of robotic platforms for the NDE of bridges. The third stream of literature highlights different algorithms for the surface- and sub-surface-level analysis of bridges that have been developed by studies in the past. A number of challenges towards the development of robotic platforms have also been discussed.

No abstract

No abstract

No abstract

Web application security is the major security concern for e-business and information sharing communities. Research showed that more than 75% attacks are being deployed at application layer and almost 90% applications are vulnerable to these attacks. Various security mechanisms in the form of signature base models, anomaly detection, scanner, firewall and intrusion detection has been proposed but ineffective to provide complete security solution at application level. These provide partial solutions are ineffective to provide defense against zero day attacks with low false positive rate. We have introduced a novel approach for effective defenses against the application level attacks. Our system use the Bayesian filter to mitigate the context base attacks which are easily eludes packet level inspection.Our intelligent system is ontology base which analyze the input semantically and capable to detect zero day attacks with negligible false positive rates. The ontology base system can be refined and extended over time. Ontology base system also help in focusing on specific portion of network packet where attack is possible, thus reduce the research space and avoid sequential search. Our system is developed by using the open source tools. Ontologies of our system specified in OWL-DL by using Protégé tool. We have used the Pallet as inference engine and Jena API as a layer of rule and reasoning.