Tube-jack and sonic testing for the evaluation of the state of stress in historical masonry

In the investigation and diagnosis of damages to historical masonry structures, the state of stress of the masonry is an important characteristic that must be determined with as much accuracy as possible. Flat-jack testing is a traditional method used to determine the state of stress in historical masonry structures. However, when irregular masonry is tested the method can cause damage to the masonry units and the accuracy of the method is reduced. An enhanced technique, called tube-jack testing, is being developed at the University of Minho to reduce the damage caused during testing and improve the accuracy when used on irregular masonry. This method uses multiple cylindrical jacks inserted in a line of holes drilled in the mortar joints of the masonry, avoiding damage to the masonry units. Concurrently with the development of tube-jack testing, the effect of stress state on sonic testing is being studied. Sonic testing is often used to determine locations of voids and damage in masonry. The focus of these studies was to determine if the state of stress is influencing the sonic test results. In this paper the results of tube-jack testing and sonic testing on masonry walls, built for the purpose of this study in the laboratory, loaded in compression is presented. The tube-jack testing is used to estimate the state of stress in the masonry and the sonic test results are evaluated based on the effect of the applied load on the wall. Future testing and study are suggested for continued development of these test methods.

Comparison of single and multi-objective evolutionary algorithms for robust link-state routing

Traffic Engineering (TE) approaches are increasingly impor- tant in network management to allow an optimized configuration and resource allocation. In link-state routing, the task of setting appropriate weights to the links is both an important and a challenging optimization task. A number of different approaches has been put forward towards this aim, including the successful use of Evolutionary Algorithms (EAs). In this context, this work addresses the evaluation of three distinct EAs, a single and two multi-objective EAs, in two tasks related to weight setting optimization towards optimal intra-domain routing, knowing the network topology and aggregated traffic demands and seeking to mini- mize network congestion. In both tasks, the optimization considers sce- narios where there is a dynamic alteration in the state of the system, in the first considering changes in the traffic demand matrices and in the latter considering the possibility of link failures. The methods will, thus, need to simultaneously optimize for both conditions, the normal and the altered one, following a preventive TE approach towards robust configurations. Since this can be formulated as a bi-objective function, the use of multi-objective EAs, such as SPEA2 and NSGA-II, came nat- urally, being those compared to a single-objective EA. The results show a remarkable behavior of NSGA-II in all proposed tasks scaling well for harder instances, and thus presenting itself as the most promising option for TE in these scenarios.