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In the last few decades, numerous methods have been developed for predicting the axial capacity of pile foundations. Among the available methods, the cone penetration test (CPT)-based models have been shown to give better predictions in many situations. This can be attributed to the fact that CPT-based methods have been developed in accordance with the CPT results, which have been found to yield more reliable soil properties; hence, more accurate axial pile capacity predictions. In this paper, one of the most commonly used artificial intelligence techniques, i.e., artificial neural networks (ANNs), is utilized in an attempt to develop artificial neural network (ANN) models that provide more accurate axial capacity predictions for driven piles and drilled shafts. The ANN models are developed using data collected from the literature and comprise 80 driven pile and 94 drilled-shaft load tests, as well as CPT results. The predictions from the ANN models are compared with those obtained from the most commonly used available CPT-based methods, and statistical analyses are carried out to rank and evaluate the performance of the ANN models and CPT methods. To facilitate the use of the developed ANN models, they are translated into simple design equations suitable for hand calculations. [ABSTRACT FROM AUTHOR]

This paper evaluates the accuracy of three commonly used models that predict accidents on two-lane, rural, arterial highways. The retrospective evaluation compared model outputs with empirical collision results for a sample of highway sections in the Province of New Brunswick. The analysis determined historical accident rates, identified key predictive variables, and compared the observed results with estimates from each safety model. All three models were found to significantly overestimate accident frequencies on the highway sections under study. The model generally employed in New Brunswick, MicroBENCOST, was found to yield the highest errors in estimated collisions. These findings suggest that the benefits from accident reduction are generally overestimated on highway improvement projects analyzed with these accident prediction models. Cet article évalue la précision de trois modèles couramment utilisés pour la prédiction d'accidents sur les autoroutes à deux voies; rurales et les avenues. L’évaluation rétrospective à comparé les résultats des modèles aux résultats empirique de collision pour un échantillon de sections d'autoroutes dans la province du Nouveau-Brunswick. L’analyse a permis de déterminer le taux historique d'accidents, a identifié les principales variables prédictives et à comparé les résultats observés aux estimations de chaque modèle de sécurité. Il a été trouvé que les trois modèles sur-estiment significativement les fréquences d’accidents sur les sections d’autoroutes étudiées. Il a été trouvé que le modèle généralement employé au Nouveau-Brunswick, MicroBENCOST, produit les erreurs les plus élevées pour l’estimation des collisions. Ces résultats suggèrent que les avantages dûs à la réduction des accidents sont généralement sur-estimés dans les projets d'amélioration des routes analysés avec ces modèles de prédiction des accidents. [ABSTRACT FROM AUTHOR]