Piles have been used as a deep foundation for both inland and offshore structures. After installation, pile capacity may increase with time. This time dependent capacity increase is known as setup, and was first mentioned in the literature in 1900 by Wendel. When accounted for accurately during the design stages, the integration of pile setup can lead to more cost-effective pile design as it will reduce pile length, pile section, and size of driving equipment. In this paper, Both Multiple Linear Regression (MLR) and Artificial Neural Networks (ANN) models were developed for predicting pile setup for three pile types (pipe, concrete, and H-pile) using 169 dynamic load tests obtained from the published literature and the authors’ files. In addition, the paper discusses the choice of variables that were examined to obtain the optimum model. Furthermore, the paper compares the predictions obtained by the developed MLR and the ANN models with those given by four traditional empirical formulae. It is concluded that the ANN model outperforms both the MLR model and the examined empirical formulae in predicting the measured pile setup. Finally, static load test data was used to further verify the developed models. It’s noted that the optimal ANN model overestimated pile capacity by 17% to 21% for the H-piles, and underestimated pile capacity by 12% to 17% for the pipe piles.

Purpose: The purpose of this paper is to develop new predictive models using gene expression programming in order to estimate the compressive strength of green concrete, as accurate models that can predict the compressive strength of green concrete are still lacking. Design/methodology/approach: To estimate the compressive strength of plain concrete, fly ash concrete, silica fume concrete and concrete with silica fume and fly ash, four predictive GEP models are developed. The GEP models are developed using a large and reliable database that is collected from the literature. The GEP models are validated using the collected experimental database. Findings: The R² is used to statistically evaluate the performance of the GEP models wherein the R² values for the GEP models including all data are 85, 95, 80 and 95.3 percent for the models that predict the compressive strength of plain concrete, fly ash concrete, silica fume concrete and concrete with silica fume and fly ash, respectively. Originality/value: The GEP models have high R² values and low RMSE and MAE, which indicates that they are capable of predicting the compressive strength of green concrete with a reasonable accuracy.

The use of roundabouts is well recognized for sustaining an efficient and safe intersection. However, the safety results may vary based on the prevailing conditions. Therefore, this study assesses the safety performance of roundabouts in Jordan. This study developed a predictive model by collecting and analyzing all accident records of 12 major urban roundabouts in the country over 3 years. For developing the model, this study employed an accident frequency analysis. The model calculated the rate of accidents and incorporated the geometric and operational characteristics of roundabouts. This was followed by ranking the safety performance of the roundabouts. It was concluded that driver behavior of violating the traffic rules, lack of clear lane markings in the circulating area and inadequate signage at the roundabouts entries are the main causes of roundabout accidents. The research recommends including the developed predictive model in future traffic control and planning studies, for identifying hazardous locations, or for prioritizing roundabout improvements based on safety performance.