Mechanistic modeling of fracture in asphalt mixtures under compressive loading

When an asphalt mixture is subjected to a destructive compressive load, it experiences a sequence of three deformation stages, as follows: the (1) primary, (2) secondary, and (3) tertiary stages. Most literature research focuses on plastic deformation in the primary and secondary stages, such as prediction of the flow number, which is in fact the initiation of the tertiary stage. However, little research effort has been reported on the mechanistic modeling of the damage that occurs in the tertiary stage. The main objective of this paper is to provide a mechanistic characterizing method for the damage modeling of asphalt mixtures in the tertiary stage. The preliminary study conducted by the writers illustrates that deformation during the tertiary flow of the asphalt mixtures is principally caused by the formation and propagation of cracks, which was signaled by the increase of the phase angle in the tertiary phase. The strain caused by the growth of cracks is the viscofracture strain, which can be obtained by conducting the strain decomposition of the measured total strain in the destructive compressive test. The viscofracture strain is employed in the research reported in this paper to mechanistically characterize the time-dependent fracture (viscofracture) of asphalt mixtures in compression. By using the dissipated pseudostrain energy-balance principle, the damage density and true stress are determined and both are demonstrated to increase with load cycles in the tertiary stage. The increased true stress yields extra viscoplastic strain, which is the reason why the permanent deformation is accelerated by the occurrence of cracks. To characterize the evolution of the viscofracture in the asphalt mixtures in compression, a pseudo J-integral Paris' law in terms of damage density is proposed and the material constants in the Paris' law are determined, which can be employed to predict the fracture of asphalt mixtures in compression. © 2013 American Society of Civil Engineers.

Dynamic causal modeling of load-dependent modulation of effective connectivity within the verbal working memory network

Neuroimaging studies have consistently shown that working memory (WM) tasks engage a distributed neural network that primarily includes the dorsolateral prefrontal cortex, the parietal cortex, and the anterior cingulate cortex. The current challenge is to provide a mechanistic account of the changes observed in regional activity. To achieve this, we characterized neuroplastic responses in effective connectivity between these regions at increasing WM loads using dynamic causal modeling of functional magnetic resonance imaging data obtained from healthy individuals during a verbal n-back task. Our data demonstrate that increasing memory load was associated with (a) right-hemisphere dominance, (b) increasing forward (i.e., posterior to anterior) effective connectivity within the WM network, and (c) reduction in individual variability in WM network architecture resulting in the right-hemisphere forward model reaching an exceedance probability of 99% in the most demanding condition. Our results provide direct empirical support that task difficulty, in our case WM load, is a significant moderator of short-term plasticity, complementing existing theories of task-related reduction in variability in neural networks. Hum Brain Mapp, 2013. © 2013 Wiley Periodicals, Inc.