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Traffic flow data are needed for traffic management and control applications as well as for transportation planning issues. Such data are usually collected from traffic sensors; however, it is not practical or even feasible to deploy traffic sensors on all of a network’s links. Instead, it is necessary to extend the information acquired from a subset of link flows to estimate the entire network’s traffic flow. To this end, this study proposes a robust deep learning architecture based on a stacked sparse autoencoders (SAEs) model for a precise estimation of the whole network’s traffic flow with an already-deployed sensor set. The proposed deep learning architecture has two consequent components: a deep learning model based on the SAEs and a fully connected layer. First, the SAEs model is used to extract traffic flow features and reach a meaningful pattern of the relation between the traffic flow data and network structure. Subsequently, the fully connected layer is used for the traffic flow estimation. Then, the whole architecture is fine-tuned to update its parameters in order to enhance the traffic flow estimation. For training the proposed deep learning architecture, synthetic link flow data are randomly generated from the network’s prior demand information. The performance of the proposed model is evaluated then validated using two real networks. A third medium real-size network is used to measure the robustness of applying the proposed methodology to this specific problem of traffic flow estimation.

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The physico-mechanical properties of rocks have crucial influence on their abrasion rates. Therefore, the objective of this study is to investigate the rock properties that govern their abrasion resistivity. For that purpose, about 132 core specimens, of granite, marble, serpentine, and breccia, were extracted from two locations in Wadi El-Miah region in the vicinity of Idfo-Mars Alam Road, South-East, Egypt. Several laboratory tests were conducted to determine the relationships between abrasion resistance and physical (i.e. porosity, density and P-wave velocity) and mechanical (i.e. uniaxial compressive strength, tensile strength and surface hardness) properties of these rocks. Subsequently, statistical analysis was employed to derive equations for estimating abrasion rates based on physico-mechanical rock properties. The results showed that the abrasion resistance decreases with increasing uniaxial compressive strength, Brazilian tensile strength; Schmidt rebound hardness, P-wave velocity, and bulk density. It was also revealed that the abrasion rate increases with increasing effective porosity. Additionally, the derived equations showed good correlations between abrasion resistance and bulk density; Schmidt rebound hardness; and tensile strength (i.e. R2 = 0.72–0.84). Thus, they can be successfully used to predict the abrasion rates. Hence, rock abrasion rate could be adopted as a tool to sort out stones for proper industrial purposes (i.e. flooring, heavy traffic, decoration, construction, building, etc.).

This paper introduces a tube-based model predictive control (MPC) for linear parameter-varying (LPV) systems which exploits knowledge about bounds on the parameters’ rate of change to extrapolate its admissible values over the prediction horizon. This information is used to construct state tubes to which the future trajectories of the state are confined. The tubes are consequently used for constraint tightening. Then, an MPC optimization problem subject to tightened sets for the state and control constraints is solved for only a nominal system corresponding to a nominal trajectory of the scheduling parameter starting from its current value. Recursive feasibility and asymptotic stability are proven and two numerical examples are given to demonstrate the effectiveness of the proposed approach.

Experimental investigation on the damage and wave propagation characteristics in PVB laminated glass panels subjected to impact loading was conducted. By applying different impact energies, the effect of two projectile configurations, a wooden post and steel ball, was studied. The effect of impact location was also investigated. The dynamic response of the panels was measured at different locations on the test panels using strain gauges. Damage and transient response and wave propagation characteristics of test panels were reported and compared. The results showed that flexural wave was the predominant wave in the response. In addition, in-plane compressive wave was also observed. It was also shown that while the projectile contact area affected the maximum transient response and crack propagation characteristics, it had a limited effect on the perforation threshold. For panels impacted at the corners, coupled strain waves were created by wave reflection processes when the waves reached the panel boundaries. Moreover, the impact velocity had a pronounced effect on peak strain and strain rates of test panels.

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