Three-dimensional in situ XCT characterisation and FE modelling of cracking in concrete

Three-dimensional (3D) characterisation and modelling of cracking in concrete have been always of great importance and interest in civil engineering. In this study, an in situ microscale X-ray computed tomography (XCT) test was carried out to characterise the 3D microscale structure and cracking behaviour under progressive uniaxial compressive loading. The 3D cracking and fracture behaviour including internal crack opening, closing, and bridging were observed through both 2D tomography slices and 3D CT images. Spatial distributions of voids and cracks were obtained to understand the overall cracking process within the specimen. Furthermore, the XCT images of the original configuration of the specimen were processed and used to build microscale realistic 3D finite element (FE) models. Cohesive interface elements were inserted into the FE mesh to capture complicated discrete crack initiation and propagation. An FE simulation of uniaxial compression was conducted and validated by the in situ XCT compression test results, followed by a tension simulation using the same image-based model to investigate the cracking behaviour. The quantitative agreement between the FE simulation and experiment demonstrates that it is a very promising and effective technique to investigate the internal damage and fracture behaviour in multiphasic composites by combining the in situ micro XCT experiment and image-based FE modelling

Characterisation of 3d fracture evolution in concrete using in-situ X-ray computed tomography testing and digital volume correlation

X-ray Computed Tomography (XCT) is a powerful technology that can accurately image the internal structures of composite and heterogeneous materials in three-dimensions (3D). In this study, in-situ micro XCT tests of concrete specimens under progressive compressive loading are carried out. The aim of the observations is to gain a better understanding of 3D fracture and failure mechanisms at the meso-scale. To characterise the fracture evolution as the deformation increases, two methods are used. The first segments the reconstructed absorption contrast XCT images using AVIZO software into different phases, namely, aggregates, mortar, cracks and voids. The second uses the digital volume correlation (DVC) technique to map the relative deformations between consecutive XCT images with high precision; bulk mechanical properties can be measured and cracks visualised via their opening displacement. The 3D crack profiles obtained by these two methods are compared, and the contributions that they can make to image-based modelling and its validation are noted

FedTracker: Furnishing Ownership Verification and Traceability for Federated Learning Model

Federated learning (FL) is a distributed machine learning paradigm allowing multiple clients to collaboratively train a global model without sharing their local data. However, FL entails exposing the model to various participants. This poses a risk of unauthorized model distribution or resale by the malicious client, compromising the intellectual property rights of the FL group. To deter such misbehavior, it is essential to establish a mechanism for verifying the ownership of the model and as well tracing its origin to the leaker among the FL participants. In this paper, we present FedTracker, the first FL model protection framework that provides both ownership verification and traceability. FedTracker adopts a bi-level protection scheme consisting of global watermark mechanism and local fingerprint mechanism. The former authenticates the ownership of the global model, while the latter identifies which client the model is derived from. FedTracker leverages Continual Learning (CL) principles to embedding the watermark in a way that preserves the utility of the FL model on both primitive task and watermark task. FedTracker also devises a novel metric to better discriminate different fingerprints. Experimental results show FedTracker is effective in ownership verification, traceability, and maintains good fidelity and robustness against various watermark removal attacks

Rethinking Closed-loop Training for Autonomous Driving

Recent advances in high-fidelity simulators have enabled closed-loop training of autonomous driving agents, potentially solving the distribution shift in training v.s. deployment and allowing training to be scaled both safely and cheaply. However, there is a lack of understanding of how to build effective training benchmarks for closed-loop training. In this work, we present the first empirical study which analyzes the effects of different training benchmark designs on the success of learning agents, such as how to design traffic scenarios and scale training environments. Furthermore, we show that many popular RL algorithms cannot achieve satisfactory performance in the context of autonomous driving, as they lack long-term planning and take an extremely long time to train. To address these issues, we propose trajectory value learning (TRAVL), an RL-based driving agent that performs planning with multistep look-ahead and exploits cheaply generated imagined data for efficient learning. Our experiments show that TRAVL can learn much faster and produce safer maneuvers compared to all the baselines. For more information, visit the project website: https://waabi.ai/research/travlComment: ECCV 202

Magma evolution during the post-rift phase of the Santos Basin, Brazil: petrogenesis and geochemistry of ∼126–121 ma basalts and diabases

The Santos Basin, a passive continental margin basin recognized for its vast deep-sea hydrocarbon potential, poses unique geological issues due to the large amount of igneous rocks revealed by drilling data. In order to understand the magmatic evolution during the post-rift phase, we studied petrology, major elements, trace elements, and Sr-Nd isotopic composition of bulk rock, and Ar-Ar dating on whole rock and minerals on basalts and diabases from Santos Basin. Ar-Ar dating results suggest that basalts and diabases emplaced on ∼126–121 Ma. The geochemistry and Sr-Nd isotopic compositions indicates the derivation of these rocks from the spinel and garnet lherzolite facies, denoted by increased La/Sm ratios that suggest a 1%–5% degree of partial melting. These findings correspond with the characteristics of continental rift basalts. The geochemical analysis hints that the older basalts and diabases were likely derived from the asthenospheric mantle, whereas the younger ones display a geochemical mix indicative of contributions from both the deeper asthenosphere and the subcontinental lithospheric mantle (SCLM), or possibly from crustal contamination. A proposed hypothetical model indicating that the deepening of the basin into the asthenosphere, in conjunction with the thinning and stretching of the lithosphere, could have been instrumental in the magmatic events recorded in the region