Numerical Analyses of Geocell-Reinforced Granular Soils under Static and Repeated Loads

Geocell is one of the geosynthetic products used primarily for soil reinforcement. It was originally developed by US Army Corps of Engineers in 1970s for quick reinforcement of cohesionless soil in the military field. Due to its unique three-dimensional geometry, geocell can provide great lateral confinement to the infill soil without relying on the interlocking or friction with the infill soil. Despite the successful applications in various types of civil engineering projects, the mechanism of geocell reinforcement has not been well understood, especially for load-supporting applications. In this study, laboratory model tests (including static load tests and moving wheel load tests) were performed to characterize the performance of the geocell-reinforced soil. A total of four types of geocell (manufactured by PRS Mediterranean, Inc.) were tested in this study. One was made from conventional high density polyethylene (HDPE) and the other three were made from novel polymer alloy (NPA). NPA is a new material characterized by flexibility at low temperatures similar to HDPE with elastic behavior similar to engineering thermoplastic. Test result showed that geocell products tested in this study can improve the modulus and strength of the granular soil under static load, as well as reduce the permanent deformation of the unpaved road under moving wheel loads. Three-dimensional numerical models were developed in this study to simulate the behavior of geocell-reinforced soil under static and repeated loads. In the model for static loading, the infill soil is modeled using a non-linear elastoplastic model (also known as the Duncan-Chang model), and the geocell was modeled using linear elastic plate model. A mechanistic-empirical model was developed for NPA geocell-reinforced soil under repeated loads. This model was developed based on the stress-dependent response model in the current mechanistic-empirical pavement design guide (MEPDG). Some modifications were made to consider (a) the three-dimensional constitutive equation of tangent resilient modulus, (b) the compaction-induced initial horizontal stress in the soil, and (c) the residual stress increase due to the accumulated permanent deformation of geocell with the number of load passes. Analyses showed that the numerical models created in this study can well simulate the experimental result from the geocell-reinforced soils. Based on the calibrated numerical models, a parametric study was performed to investigate the effects from various factors, i.e., (a) thickness of the geocell-reinforced layer, (b) geocell modulus, (c) subgrade stiffness and strength, (d) interface shear modulus, and (e) infill material modulus. The mechanistic-empirical design methods for paved and unpaved roads with geocell-reinforced bases were also discussed

Effectuation and Co-creation in Social Enterprises: Examining the Relationship in the Context of Uncertainty

This study attempts to adjudicate the correlation between effectuation and co-creation in the context of social enterprises in India. The environments in which social entrepreneurs operate are highly uncertain, given the lack of institutional support in their operations. Although scholars have attempted to define approaches and strategies for scaling social enterprises, we propose in this study the relevance of effectuation logic and its subsequent effect on co-creation in building social enterprises, whereas it is observed that other linear factors like government support and slack resources are moderated by non-linear variable like uncertainty. To achieve this, we consider government support and slack resources as environmental and firm-level factors affecting the performance of social enterprises. We hypothesize that government support and slack resources positively correlate with effectuation logic under the influence of uncertainty. Empirical data were collected from 181 social enterprises throughout India and the results are analyzed towards the objectives of our hypotheses

FSRNet: End-to-End Learning Face Super-Resolution with Facial Priors

Face Super-Resolution (SR) is a domain-specific super-resolution problem. The specific facial prior knowledge could be leveraged for better super-resolving face images. We present a novel deep end-to-end trainable Face Super-Resolution Network (FSRNet), which makes full use of the geometry prior, i.e., facial landmark heatmaps and parsing maps, to super-resolve very low-resolution (LR) face images without well-aligned requirement. Specifically, we first construct a coarse SR network to recover a coarse high-resolution (HR) image. Then, the coarse HR image is sent to two branches: a fine SR encoder and a prior information estimation network, which extracts the image features, and estimates landmark heatmaps/parsing maps respectively. Both image features and prior information are sent to a fine SR decoder to recover the HR image. To further generate realistic faces, we propose the Face Super-Resolution Generative Adversarial Network (FSRGAN) to incorporate the adversarial loss into FSRNet. Moreover, we introduce two related tasks, face alignment and parsing, as the new evaluation metrics for face SR, which address the inconsistency of classic metrics w.r.t. visual perception. Extensive benchmark experiments show that FSRNet and FSRGAN significantly outperforms state of the arts for very LR face SR, both quantitatively and qualitatively. Code will be made available upon publication.Comment: Chen and Tai contributed equally to this pape