Bashar Tarawneh

New Inspection and Risk Assessment Methods for Metal Highway Culverts in Ohio

This paper first describes Ohios new statewide culvert management program, which aimed to reduce the risk of structural failure of culverts that serve major highways. The paper next describes a research project conducted by the Ohio Research Institute for Transportation and the Environment (ORITE) research team at Ohio University to validate the effectiveness of the approaches outlined in the new program. The new culvert program is supported by high-resolution field inspection and rating procedures for concrete, metal, and thermoplastic culverts. This paper focuses on the metal culvert components of the research project. The new field inspection procedure for metal culverts was applied at 25 sites to detect problems common to many metal culvert sites in Ohio. The data produced during the field inspection phase were analyzed statistically to evaluate the effectiveness of the new procedure and to verify the findings made by the previous study about the conditions of the metal culverts that serve Ohios maj...

Inspection and Risk Assessment of Mechanically Stabilized Earth Walls Supporting Bridge Abutments

AbstractUsing a mechanically stabilized earth (MSE) wall as a bridge abutment has become an attractive option to the traditional cast-in-place wall abutment. The use of an MSE wall abutment notably...

Structural Behavior of Concrete Box Culverts under Deep Burial

AbstractCulverts have been used to transmit surface drainage under roadways in place of typical short-span bridges because they provide an economical construction system. In this paper, a numerical...

Gene expression programming model to predict driven pipe piles set-up

Abstract In this paper, Gene Expression Programming (GEP) is developed to predict the increase in pipe piles axial capacity with time known as set-up. Pile set-up is significant and continues to increase with time after installation. Analysis of the collected data identified pipe pile diameter, time after installation, soil type, effective vertical effective stress at the pile tip and the initial axial capacity as playing a substantial role in predicting pile set-up. The results indicated that GEP could predict the pipe pile set-up with a remarkable accuracy (R 2 = 0.95, MAE = 377.5 and RMSE = 484.25) for the training data-set. The developed GEP model is provided in the pattern of expression tree and transformed into a mathematical equation. A statistical performance comparison is presented between the developed GEP model and previously published empirical formulas. Based on the results, the GEP model is substantially superior the traditional empirical relationships and has been identified to provide better solutions to predict set-up.