Culverts are important components of highway infrastructure. They are structurally designed to support earth and live traffic loads. Corrugated steel pipes (CSPs) are widely used as culverts in North America in different geometries. However, due to the corrosive nature of the stormwater passing through the culverts, it is common to find CSPs with partially or entirely lost inverts. Dependent on site, depth of cover and embedment conditions, invert deterioration would not necessarily result in culvert failure. This paper presents the results of a laboratory testing campaign that evaluates the structural capacity of two circular CSPs and their equivalent arch CSP through the application of a vertical static loading. The circular pipe samples had a length of 6 ft (1.82 m) and a diameter of 60 in. (1.52 m). The same length arch pipe sample had a span of 71 in. (1.8 m) and a rise of 47 in. (1.19 m). The invert of the arch and one of the circular CSPs were cut to simulate heavily corroded culverts in service. The pipe samples were embedded under two feet (0.6 m) of cover using one foot (0.3 m) of sand and one foot (0.3 m) of coarse aggregates on top, simulating a base course layer of pavement. The results of testing showed that the invert-cut circular CSP was highly dependent on its ring stiffness. While, the invert-cut arch CSP took advantage of its arch geometry and was able to resist the applied load without significant loss in the sample pipe’s horizontal dimension.

This study presents a random shape aggregate model by establishing a functional mixture model for images of aggregate shapes. The mesoscale simulation to consider heterogeneous properties concrete is the highly cost- and time-effective method to predict the mechanical behavior of the concrete. Due to the significance of the design of the mesoscale concrete model, the shape of the aggregate is the most important parameter to obtain a reliable simulation result. We propose image analysis and functional data clustering for random shape aggregate models (IFAM). This novel technique learns the morphological characteristics of aggregates using images of real aggregates as inputs. IFAM provides random aggregates across a broad range of heterogeneous shapes using samples drawn from the estimated functional mixture model as outputs. Our learning algorithm is fully automated and allows flexible learning of the complex characteristics. Therefore, unlike similar studies, IFAM does not require users to perform time-consuming tuning on their model to provide realistic aggregate morphology. Using comparative studies, we demonstrate the random aggregate structures constructed by IFAM achieve close similarities to real aggregates in an inhomogeneous concrete medium. Thanks to our fully data-driven method, users can choose their own libraries of real aggregates for the training of the model and generate random aggregates with high similarities to the target libraries.

Spray applied pipe lining (SAPL) is a trenchless technology renewal technique for gravity flow conduits such as culverts, storm drain, storm sewe | POSTER

Spray applied pipe lining (SAPL) is a trenchless technology method used to renew deteriorated gravity storm conduits and culverts. Currently, the | POSTER

Spray applied pipe liner (SAPL) is used by Departments of Transportation (DOTs) as a trenchless technology methodology for renewal of deteriorate | POSTER