Ishwarya Srikanth

This study developed a noncontact deflection monitoring technique using terrestrial laser scanner (TLS) and images for faster, safer, and efficient 2D/3D bridge deflection measurements. The technique was applied to assess the serviceability performance of two timber trestle railroad bridges located in Florida and validated based on bridge deflections from deflectometers. A key challenge includes extracting control points that are visible both in images and TLS data due to different modalities of the data. Hence, this study presents a method of extracting linear features from both images and TLS data. The camera pose was derived from images and TLS data by using a linear feature-based registration algorithm. The deformations in the structure were then detected by measuring the points of interest for different loads. There are several unique contributions of the study. First, there is no requirement for the use of targets which improves the safety of bridge engineers. Second, it is a relatively cost-effective technique for obtaining bridge deflections due to moving train loads, as the use of a laser scanner as part of the noncontact technique is only once in a lifetime for a given bridge. Alternatively, total stations can also be used to capture linear features. Finally, this technique produced accurate deflection measurements using a linear feature-based registration technique. This study found that the noncontact deflection monitoring technique agrees well with actual deformations observed from deflectometers.

Distresses in concrete and timber bridge components occur due to physical, chemical and mechanical factors that manifest as observable symptoms of deterioration. Deterioration models relate the observable defects to the several factors affecting the deterioration. Only limited studies exist on the development of non-parametric (NP) models for bridge management, which have the unique advantage of not requiring any assumption on the functional form of the models. To address this research gap, this paper develops non-parametric deterioration models for risk and reliability assessments of concrete and timber bridges based on two methods: (1) average time in condition rating (ATICR), and (2) Kaplan-Meier (K-M) estimates. This study proposes relative deterioration rates based on the ATICR of various concrete and timber bridge components. The methodology for developing NP deterioration models based on K-M estimates for probabilistic prediction of bridge safety while accounting for the partial information from the incomplete bridge condition observations is explained. This study also investigates the effect of multiple factors affecting the bridge component condition, including aging, traffic loads, environmental conditions, and design characteristics, and estimates the median survival years for reinforced concrete decks.

This paper develops a non-contact deflection monitoring technique using Terrestrial Laser Scanner (TLS) and images for faster, safer, and efficient 2D/3D bridge deflection measurements. The technique is employed in assessing the serviceability performance of two timber trestle railroad bridges located in Florida and validated based on bridge deflections from deflectometers. A key challenge includes extracting control points that are visible both in images and TLS data due to different modalities of the data. Hence, this study presents a method of extracting linear features from both images and TLS data. The camera pose is derived from images and TLS data by using a linear feature-based registration algorithm. The deformations in the structure are then detected by measuring the points of interest for different loads. The unique contributions of the study include: i) no requirement for the use of targets which improves the safety of bridge engineers, ii) relatively cost-effective technique for obtaining bridge deflections due to moving train loads since the use of a laser scanner as part of the non-contact technique is only once in a lifetime for a given bridge. Alternatively, total stations can also be used to capture linear features., iii) accurate deflection measurement using linear feature-based registration technique. This study concludes that the non-contact deflection monitoring technique agrees well with actual deformations observed from deflectometers.

Concrete highway bridge remaining service life has been at the forefront of bridge owners’ concerns for several years in terms of growing bridge maintenance needs. Distresses in concrete bridge components occur due to physical, chemical, and mechanical factors that manifest as observable symptoms of deterioration. Deterioration models realistically link the observable defects to the several explanatory variables affecting the deterioration that aid in the planning of appropriate corrective actions. Only a limited number of explanatory variables have been considered in the published literature in the development of bridge component deterioration models. This study investigates the effect of multiple relevant explanatory variables affecting the bridge component condition including aging, loads and environmental conditions, and design characteristics using a multivariate regression analysis approach. Deterioration models for both superstructure and substructure of reinforced and prestressed concrete bridges are developed and validated in the present study. The proposed models will aid the bridge engineers to predict the concrete bridge component conditions based on bridge characteristics for planning preventive maintenance.