Truck Volume Estimation via Linear Regression Under Limited Data

This paper employs linear regression algorithms in order to train models under the presence of limited training data. Usually in transportation applications, these models are built via Ordinary Least Squares and Stepwise Regression, which perform poorly under limited data. The algorithms presented in this paper have been extensively used in other scientific fields for problems with similar conditions and seem to partially or fully remedy this problem and its consequences. Four different algorithms are presented and several models are built. The models are used for truck volume prediction on highway sections in New Jersey, and results are compared to Stepwise Linear regression models

Truck Volume Estimation via Linear Regression Under Limited Data

This paper employs linear regression algorithms in order to train models under the presence of limited training data. Usually in transportation applications, these models are built via Ordinary Least Squares and Stepwise Regression, which perform poorly under limited data. The algorithms presented in this paper have been extensively used in other scientific fields for problems with similar conditions and seem to partially or fully remedy this problem and its consequences. Four different algorithms are presented and several models are built. The models are used for truck volume prediction on highway sections in New Jersey, and results are compared to Stepwise Linear regression models

Truck Volume Estimation via Linear Regression Under Limited Data

This paper employs linear regression algorithms in order to train models under the presence of limited training data. Usually in transportation applications, these models are built via Ordinary Least Squares and Stepwise Regression, which perform poorly under limited data. The algorithms presented in this paper have been extensively used in other scientific fields for problems with similar conditions and seem to partially or fully remedy this problem and its consequences. Four different algorithms are presented and several models are built. The models are used for truck volume prediction on highway sections in New Jersey, and results are compared to Stepwise Linear regression models

Truck Volume Estimation via Linear Regression Under Limited Data

This paper employs linear regression algorithms in order to train models under the presence of limited training data. Usually in transportation applications, these models are built via Ordinary Least Squares and Stepwise Regression, which perform poorly under limited data. The algorithms presented in this paper have been extensively used in other scientific fields for problems with similar conditions and seem to partially or fully remedy this problem and its consequences. Four different algorithms are presented and several models are built. The models are used for truck volume prediction on highway sections in New Jersey, and results are compared to Stepwise Linear regression models

Application of X-Shaped CFRP Ropes for Structural Upgrading of Reinforced Concrete Beam–Column Joints under Cyclic Loading–Experimental Study

The effectiveness of externally applied fiber-reinforced polymer (FRP) ropes made of carbon fibers in X-shape formation and in both sides of the joint area of reinforced concrete (RC) beam–column connections is experimentally investigated. Six full-scale exterior RC beam–column joint specimens are tested under reverse cyclic deformation. Three of them have been strengthened using carbon FRP (CFRP) ropes that have been placed diagonally in the joint as additional, near surface-mounted reinforcements against shear. Full hysteretic curves, maximum applied load capacity, damage modes, stiffness and energy dissipation values per each loading step are presented and compared. Test results indicated that joint sub assemblages with X-shaped CFRP ropes exhibited improved hysteretic behavior and ameliorated performance with respect to the reference specimens. The effectiveness and the easy-to-apply character of the presented strengthening technique is also discussed