The long-term monitoring and performance evaluation techniques for the steel strand based pre-stressed structures are
still not mature yet, especially for the prestressing loss monitoring and prediction. The main problem of this issue is lack
of reliable monitoring techniques. To resolve this problem, in this paper, a new kind of quasi-distributed smart steel
strand based on FRP-OFBG(Fiber Reinforced Polymer-Optical Fiber Bragg Grating) has been developed and its
pre-stress monitoring principle has been also given. The test of the post-tension pre-stressed concrete beam with bonded
tendons and its tensioning experiments have been conducted. And the prestressing loss of the steel strands has been
monitored using the FBG in it. Researches results indicate that this kind of smart steel strand can monitor both instant
loss and permanent loss of the prestressing successfully, and it can preferably describe the pre-stress loss state of the
pre-stressed structure. Compared with the traditional monitoring instrument, this kind of smart steel strand owns distinct
advantages and broad application foregrounds.
This paper presents a combined, two-block framework for unsupervised image segmentation, which is capable of leveraging the best qualities of the watershed transform and MRF models and taking advantage of multi-cue information. The first block extracts various features that respond to different cues of the image and generates their gradient images. Then the obtained gradient images are combined to form a single-valued gradient surface, whose watershed transform provides over-segmented, but homogeneous image regions. The second block of our algorithm groups together these primitive regions into meaningful object based on an improved MRF model. The proposed algorithm is compared with other traditional methods in segmentation of Brodatz texture mosaics and real multi-spectral image. The satisfying experimental results demonstrate the better performance of our new framework.