Polypropylene fiber reinforced concrete in railway crossties

Cracking of concrete crossties is a performance problem that reduces service life and increases maintenance costs. While strong in compression, plain concrete is relatively weak and brittle under tensile stresses. Inclusion of synthetic polypropylene macro fibers in concrete is known to improve crack resistance and is a feasible solution for prolonging the life of crossties. The present study investigated the performance of synthetic polypropylene macro fiber reinforced concrete and their application in railway crossties. The study involved a thorough review of the properties and testing of synthetic polypropylene fiber reinforced concrete (FRC). A standard test method for obtaining average residual strength of FRC was used to evaluate the performance of various concrete mixtures reinforced with synthetic polypropylene macro fibers. It was found out that the concrete with higher fiber proportions showed significantly higher residual load carrying capacity (post-cracking response). Moreover, the concrete mixtures had acceptable workability and showed only slight loss in compressive strength due to inclusion of fibers. Self-consolidating concrete (SCC) is an emerging class of concrete which flows and consolidates on its own without vibration. Fiber reinforcement can be used in SCC to enhance the mechanical properties of concrete. The present study investigated the rheological and mechanical properties of SCC reinforced with different proportions of fibers. Fresh property tests included slump flow test and rheological tests using a concrete rheometer. The study underscored the potential for fibers to be accommodated by adjusting the mixture proportions of concrete. It was shown that inclusion of fibers in SCC is feasible for the purpose of manufacturing structural elements like railway crossties. The present study also considered the current state of prestressed concrete crosstie design and the impact of FRC on mechanical performance of concrete crossties. The applicability of FRC in railway crossties was investigated by developing and testing prototype crossties. A comparative study was performed between a conventional crosstie and a fiber reinforced crosstie through tests at rail seat and center of crosstie. It was found out that the synthetic polypropylene fibers provided sustained capacity for deformation in the concrete crossties along with an improved crack resistance. Lastly, this study developed a tensile stress-strain model for FRC behavior. Four point bending test results of FRC beams were used to determine tensile behavior of FRC using an inverse analysis approach and a back calculator tool. Preliminary tensile stress-strain models were established which can be used to define constitutive properties for concrete when using finite element analysis (FEA) to analyze experimental results. FEA has not been performed as a part of this thesis work, but will be pursued in subsequent research activities at the University of Illinois

Supervised Regularized Canonical Correlation Analysis: integrating histologic and proteomic measurements for predicting biochemical recurrence following prostate surgery

Abstract Background Multimodal data, especially imaging and non-imaging data, is being routinely acquired in the context of disease diagnostics; however, computational challenges have limited the ability to quantitatively integrate imaging and non-imaging data channels with different dimensionalities and scales. To the best of our knowledge relatively few attempts have been made to quantitatively fuse such data to construct classifiers and none have attempted to quantitatively combine histology (imaging) and proteomic (non-imaging) measurements for making diagnostic and prognostic predictions. The objective of this work is to create a common subspace to simultaneously accommodate both the imaging and non-imaging data (and hence data corresponding to different scales and dimensionalities), called a metaspace. This metaspace can be used to build a meta-classifier that produces better classification results than a classifier that is based on a single modality alone. Canonical Correlation Analysis (CCA) and Regularized CCA (RCCA) are statistical techniques that extract correlations between two modes of data to construct a homogeneous, uniform representation of heterogeneous data channels. In this paper, we present a novel modification to CCA and RCCA, Supervised Regularized Canonical Correlation Analysis (SRCCA), that (1) enables the quantitative integration of data from multiple modalities using a feature selection scheme, (2) is regularized, and (3) is computationally cheap. We leverage this SRCCA framework towards the fusion of proteomic and histologic image signatures for identifying prostate cancer patients at the risk of 5 year biochemical recurrence following radical prostatectomy. Results A cohort of 19 grade, stage matched prostate cancer patients, all of whom had radical prostatectomy, including 10 of whom had biochemical recurrence within 5 years of surgery and 9 of whom did not, were considered in this study. The aim was to construct a lower fused dimensional metaspace comprising both the histological and proteomic measurements obtained from the site of the dominant nodule on the surgical specimen. In conjunction with SRCCA, a random forest classifier was able to identify prostate cancer patients, who developed biochemical recurrence within 5 years, with a maximum classification accuracy of 93%. Conclusions The classifier performance in the SRCCA space was found to be statistically significantly higher compared to the fused data representations obtained, not only from CCA and RCCA, but also two other statistical techniques called Principal Component Analysis and Partial Least Squares Regression. These results suggest that SRCCA is a computationally efficient and a highly accurate scheme for representing multimodal (histologic and proteomic) data in a metaspace and that it could be used to construct fused biomarkers for predicting disease recurrence and prognosis.</p