An Experimental Validation of a Statistical-Based Damage Detection Approach, January 2011

In this work, a previously-developed, statistical-based, damage-detection approach was validated for its ability to autonomously detect damage in bridges. The damage-detection approach uses statistical differences in the actual and predicted behavior of the bridge caused under a subset of ambient trucks. The predicted behavior is derived from a statistics-based model trained with field data from the undamaged bridge (not a finite element model). The differences between actual and predicted responses, called residuals, are then used to construct control charts, which compare undamaged and damaged structure data. Validation of the damage-detection approach was achieved by using sacrificial specimens that were mounted to the bridge and exposed to ambient traffic loads and which simulated actual damage-sensitive locations. Different damage types and levels were introduced to the sacrificial specimens to study the sensitivity and applicability. The damage-detection algorithm was able to identify damage, but it also had a high false-positive rate. An evaluation of the sub-components of the damage-detection methodology and methods was completed for the purpose of improving the approach. Several of the underlying assumptions within the algorithm were being violated, which was the source of the false-positives. Furthermore, the lack of an automatic evaluation process was thought to potentially be an impediment to widespread use. Recommendations for the improvement of the methodology were developed and preliminarily evaluated. These recommendations are believed to improve the efficacy of the damage-detection approach.

Development of a Database for Drilled Shaft Foundation Testing (DSHAFT), June 2012

Drilled shafts have been used in the US for more than 100 years in bridges and buildings as a deep foundation alternative. For many of these applications, the drilled shafts were designed using the Working Stress Design (WSD) approach. Even though WSD has been used successfully in the past, a move toward Load Resistance Factor Design (LRFD) for foundation applications began when the Federal Highway Administration (FHWA) issued a policy memorandum on June 28, 2000.The policy memorandum requires all new bridges initiated after October 1, 2007, to be designed according to the LRFD approach. This ensures compatibility between the superstructure and substructure designs, and provides a means of consistently incorporating sources of uncertainty into each load and resistance component. Regionally-calibrated LRFD resistance factors are permitted by the American Association of State Highway and Transportation Officials (AASHTO) to improve the economy and competitiveness of drilled shafts. To achieve this goal, a database for Drilled SHAft Foundation Testing (DSHAFT) has been developed. DSHAFT is aimed at assimilating high quality drilled shaft test data from Iowa and the surrounding regions, and identifying the need for further tests in suitable soil profiles. This report introduces DSHAFT and demonstrates its features and capabilities, such as an easy-to-use storage and sharing tool for providing access to key information (e.g., soil classification details and cross-hole sonic logging reports). DSHAFT embodies a model for effective, regional LRFD calibration procedures consistent with PIle LOad Test (PILOT) database, which contains driven pile load tests accumulated from the state of Iowa. PILOT is now available for broader use at the project website: http://srg.cce.iastate.edu/lrfd/. DSHAFT, available in electronic form at http://srg.cce.iastate.edu/dshaft/, is currently comprised of 32 separate load tests provided by Illinois, Iowa, Minnesota, Missouri and Nebraska state departments of transportation and/or department of roads. In addition to serving as a manual for DSHAFT and providing a summary of the available data, this report provides a preliminary analysis of the load test data from Iowa, and will open up opportunities for others to share their data through this quality–assured process, thereby providing a platform to improve LRFD approach to drilled shafts, especially in the Midwest region.

A-1 - Monthly Public Assistance Statistical Report Family Investment Program, July 2013

A-1 - Monthly Public Assistance Statistical Report Family Investment Program

Statistical Modeling of the Eye for Multimodal Treatment Planning for External Beam Radiation Therapy of Intraocular Tumors.

PURPOSE: Ocular anatomy and radiation-associated toxicities provide unique challenges for external beam radiation therapy. For treatment planning, precise modeling of organs at risk and tumor volume are crucial. Development of a precise eye model and automatic adaptation of this model to patients' anatomy remain problematic because of organ shape variability. This work introduces the application of a 3-dimensional (3D) statistical shape model as a novel method for precise eye modeling for external beam radiation therapy of intraocular tumors. METHODS AND MATERIALS: Manual and automatic segmentations were compared for 17 patients, based on head computed tomography (CT) volume scans. A 3D statistical shape model of the cornea, lens, and sclera as well as of the optic disc position was developed. Furthermore, an active shape model was built to enable automatic fitting of the eye model to CT slice stacks. Cross-validation was performed based on leave-one-out tests for all training shapes by measuring dice coefficients and mean segmentation errors between automatic segmentation and manual segmentation by an expert. RESULTS: Cross-validation revealed a dice similarity of 95% ± 2% for the sclera and cornea and 91% ± 2% for the lens. Overall, mean segmentation error was found to be 0.3 ± 0.1 mm. Average segmentation time was 14 ± 2 s on a standard personal computer. CONCLUSIONS: Our results show that the solution presented outperforms state-of-the-art methods in terms of accuracy, reliability, and robustness. Moreover, the eye model shape as well as its variability is learned from a training set rather than by making shape assumptions (eg, as with the spherical or elliptical model). Therefore, the model appears to be capable of modeling nonspherically and nonelliptically shaped eyes.

A novel in vitro metabolomics approach for neurotoxicity testing, proof of principle for methyl mercury chloride and caffeine

There is a need for more efficient methods giving insight into the complex mechanisms of neurotoxicity. Testing strategies including in vitro methods have been proposed to comply with this requirement. With the present study we aimed to develop a novel in vitro approach which mimics in vivo complexity, detects neurotoxicity comprehensively, and provides mechanistic insight. For this purpose we combined rat primary re-aggregating brain cell cultures with a mass spectrometry (MS)-based metabolomics approach. For the proof of principle we treated developing re-aggregating brain cell cultures for 48h with the neurotoxicant methyl mercury chloride (0.1-100muM) and the brain stimulant caffeine (1-100muM) and acquired cellular metabolic profiles. To detect toxicant-induced metabolic alterations the profiles were analysed using commercial software which revealed patterns in the multi-parametric dataset by principal component analyses (PCA), and recognised the most significantly altered metabolites. PCA revealed concentration-dependent cluster formations for methyl mercury chloride (0.1-1muM), and treatment-dependent cluster formations for caffeine (1-100muM) at sub-cytotoxic concentrations. Four relevant metabolites responsible for the concentration-dependent alterations following methyl mercury chloride treatment could be identified using MS-MS fragmentation analysis. These were gamma-aminobutyric acid, choline, glutamine, creatine and spermine. Their respective mass ion intensities demonstrated metabolic alterations in line with the literature and suggest that the metabolites could be biomarkers for mechanisms of neurotoxicity or neuroprotection. In addition, we evaluated whether the approach could identify neurotoxic potential by testing eight compounds which have target organ toxicity in the liver, kidney or brain at sub-cytotoxic concentrations. PCA revealed cluster formations largely dependent on target organ toxicity indicating possible potential for the development of a neurotoxicity prediction model. With such results it could be useful to perform a validation study to determine the reliability, relevance and applicability of this approach to neurotoxicity screening. Thus, for the first time we show the benefits and utility of in vitro metabolomics to comprehensively detect neurotoxicity and to discover new biomarkers.