The most common recycled construction materials used as unbound base course in pavement construction are recycled concrete aggregate (RCA) and recycled asphalt pavement (RAP). This study investigated the mechanical properties of RCA and RAP as unbound base, including the relationships between resilient modulus (Mr) and composition (e.g., particle shape, binder type, aggregate mineralogy, and contamination). The recycled materials were collected from a wide geographical area, covering eight states in the U.S. The Mr tests were conducted following National Cooperative Highway Research Program (NCHRP) protocol, which measures specimen deflections externally and internally. Power function and NCHRP models were applied to estimate Mr. The NCHRP model was more reliable in capturing Mr dependency on stress state in RCA and RAP. A multiple linear regression model was developed to predict the Mr of RCA (R2=0.96) and RAP (R2=0.97). There was a high degree of correlation between the predicted Mr and the physical properties of RCA (R2=0.89) and RAP (R2=0.99).

Effectiveness of cement kiln dust (CKD) in improving the stiffness of recycled base course materials was studied using both seismic modulus and bench-scale resilient modulus tests. Recycled materials included road surface gravel (RSG) and recycled pavement material (RPM). The modulus of RPM and RSG specimens mixed with CKD increased 5–30 times compared with untreated materials; however, the improvement was not as high as cement stabilization. Modulus generally increased with curing time with more hydration; however, decrease in the modulus of the RPM mixed with 15% CKD during curing is attributed to swelling potential of the CKD. Lower rate of increase in modulus of CKD mixtures compared with cement mixtures with curing time was attributable to the chemical composition of CKD, i.e., high free lime and sulfate contents. Freeze-thaw durability tests resulted in modulus reduction on the order of 0.5 to 0.8 for CKD mixtures and 0.5 for cement mixtures. Attributable to the combined effects of stiffness...

This study aimed to develop a deep neural network model for predicting the soil water content and bulk density of soil based on features extracted from in situ soil surface images. Soil surface images were acquired using a Canon EOS 100d camera. The camera was installed in the vertical direction above the soil surface layer. To maintain uniform illumination conditions, a dark room and LED lighting were utilized. Following the acquisition of soil surface images, soil samples were collected using a metal cylinder to obtain measurements of soil water content and bulk density. Various features were extracted from the images, including color, texture, and shape features, and used as inputs for both a multiple regression analysis and a deep neural network model. The results show that the deep neural network regression model can predict soil water content and bulk density with root mean squared error of 1.52% and 0.78 kN/m³. The deep neural network model outperformed the multiple regression analysis, achieving a high accuracy for predicting both soil water content and bulk density. These findings suggest that in situ soil surface images, combined with deep learning techniques, can provide a fast and reliable method for predicting important soil properties.

For appropriate managing fields and crops, it is essential to understand soil properties. There are drawbacks to the conventional methods currently used for collecting a large amount of data from agricultural lands. Convolutional neural network is a deep learning algorithm that specializes in image classification, and developing soil property prediction techniques using this algorithm will be extremely beneficial to soil management. We present the convolution neural network models for estimating water content and dry density using soil surface images. Soil surface images were taken with a conventional digital camera. The range of water content and dry density were determined considering general upland soil conditions. Each image was divided into segmented images and used for model training and validation. The developed model confirmed that the model can learn soil features through appropriate image argumentation of few of original soil surface images. Additionally, it was possible to predict the soil water content in a situation where various soil dry density conditions were considered.