Hot-mix asphalt (HMA) is a composite material consisting of stone-aggregates, sand, asphalt binder and additives. The properties of this combined material are dependent on the volumetric parameters used in the mix design. This study investigates the effects of volumetric mix factors on the dynamic moduli (E*) of eleven categories of HMAs. For each category of asphalt mixture, the variations in dynamic modulus for different contractors, binder types, effective binder content (Vbe), air void (Va), voids-in-mineral aggregates (VMA), voids-filled-with asphalt (VFA) and asphalt content (AC) are assessed statistically. Results show that the S(100) mixture (nominal size of 19 mm, 100 gyrations) with the Performance Grade (PG) binder of PG 64-22 has the highest value of E* at low temperature or high reduced frequency. At high temperature or lower reduced frequency, S(100) PG 76-28 has the highest E* value. The SX(75) mixture (nominal size of 12.5 mm, 75 gyrations) with the binder of PG 64-28 has the lowest E* value at high temperature or lower reduced frequency. At low temperature or high reduced frequency, SX(75) PG 58-34 has the lowest E* value. The Stone Mix Asphalt (SMA) mix has a lower E* compared to S(100) and SX(100) mixes ((nominal size of 12.5 mm, 100 gyrations) with the Performance Grade (PG) binder of) at low temperature. The E* increases with an increase in Vbe, Va, and VFA, and decreases with an increase in VMA and AC. The E* of a mix can vary from 200 ksi (1380 MPa) to about 1000 ksi (6900 MPa) for a particular frequency (10 Hz) and temperature (21.1 °C), even if samples are from the same contractor.

In the design of pavement infrastructure, the flow number is used to determine the suitability of a hot-mix asphalt mixture (HMA) to resist permanent deformation when used in flexible pavement. This study investigates the sensitivity of the flow numbers to the mix factors of eleven categories of HMAs used in flexible pavements. A total of 105 specimens were studied for these eleven categories of HMAs. For each category of asphalt mixture, the variations in flow number for different contractors, binder types, effective binder contents, air voids, voids in mineral aggregates, voids filled with asphalt, and asphalt contents were assessed statistically. The results show that the flow numbers for different types of HMA used in Colorado vary from 47 to 2272. The same mix may have statistically different flow numbers, regardless of the contractor. The flow number increases with increasing effective binder content, air voids, voids in mineral aggregates, voids filled with asphalt, and asphalt content in the study range of these parameters.

Peer tutoring is established as one of the most efficient methods in learning. The same approach blended with diversity is used in this study through in-class-study-group formation. The effectiveness of group study in enhancing student performance is investigated in this paper. The study is done in fundamental engineering classes in two US regional universities over the last two years. One institution is a traditional mainstream university and other is a Hispanic Serving Institution (HSI) with a diverse student population. In general, engineering has very few students from underrepresented minority backgrounds. Low performance rate of minority students becoming a challenge for engineering programs in HSIs since minority students are the majority of the student body. Success of the entire engineering program will largely depend on the success rate of the minority students.

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Recognition of human intention is crucial and challenging due to subtle motion patterns of a series of action evolutions. Understanding of human actions is the foundation of many applications, i.e., human robot interaction, smart video monitoring and autonomous driving etc. Existing deep learning methods use either spatial or temporal features during training. This research focuses on developing a lightweight method using both spatial and temporal features to predict human intention correctly. This research proposes Convolutional Long Short-Term Deep Network (CLSTDN) consists of Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). CNN uses Inception-ResNet-v2 to classify object specific class categories by extracting spatial features and RNN uses Long Short-Term Memory (LSTM) for final prediction based on temporal features. Proposed method was validated on four challenging benchmark dataset, i.e., UCF Sports, UCF-11, KTH and UCF-50. Performance of the proposed method was evaluated using seven performance metrics, i.e., accuracy, precision, recall, f-measure, error rate, loss and confusion matrix. Proposed method showed better results comparing with existing research results. Proposed method is expected to encourage researchers to use in future for real time implications to predict human intentions more robustly.