Redundancy, Reliability Updating, and Risk-Based Maintenance Optimization of Aging Structures

The presence of uncertainties in the structural design process requires the incorporation of system reliability and redundancy concepts in the design specifications. AASHTO Load and Resistance Factor Design specifications utilize a factor relating to redundancy from the load side in the strength limit state to account for system redundancy in the component design. However, the classification of the component redundancy level is very general and the evaluation of values for this factor is also very subjective. Moreover, this factor does not account for several parameters that have significant effects on the system redundancy. Therefore, there is room for further improvement in the classification of the redundancy level and quantification of the associated values. Structural safety is of paramount importance during the entire lifetime of a structure. Aggressive environmental conditions such as corrosion and / or extreme events such as earthquakes and scour can cause a reduced level of structural safety and functionality under uncertainties. For this reason, assessment of structural performance using probabilistic performance measures such as reliability, redundancy and risk is necessary to determine if maintenance actions need to be applied. Due to the financial constraints on the maintenance budget, optimization tools should be incorporated in the structural maintenance process for seeking the effective and economical solution. The accuracy of performance assessment affects the efficiency of decision making on the maintenance. To enhance the accuracy of the assessment results, objective data from structural health monitoring can be integrated with the prior information on resistances and / or load effects to obtain a better estimation. The main objective of this study is two-fold: firstly, to propose a redundancy factor considering the effects of several parameters to provide a rational reliability-based design of structural components; secondly, to develop general approaches for integrating the reliability- and risk-based performance indicators in the life-cycle management framework for structures. Redundancy factors for a wide range of systems consisting of different number of components are evaluated considering several correlation cases. An approach for evaluating time-variant reliability, redundancy, direct and indirect risk considering the effects of resistance deterioration, system modeling type and correlations among failure modes of components is proposed. A risk-based approach for optimum maintenance of bridges under traffic and earthquake loads is also developed. Furthermore, a methodology for assessing risk caused by partially or fully closure of bridge lanes due to traffic load and scour is proposed. Finally, approaches for incorporating structural heath monitoring data in the reliability and redundancy assessment of ship structures by updating one and two parameters using Bayesian method are developed. The proposed new definition of redundancy factor improves the classification of redundancy levels of structural components and quantification of the factor relating to redundancy used in the current AASHTO Load and Resistance Factor Design specifications by considering several parameters which have significant effects on structural redundancy. The direct, indirect and total risks caused by component failure based on the developed event-tree model can provide guidance on determining the maintenance priorities of bridge components. The proposed approaches for assessing the time-variant risk due to bridge failure / lanes closure under traffic and earthquake / scour hazards can be efficiently used for obtaining lifetime risk profiles based on which the optimum risk mitigation strategies can be determined through the proposed risk-based optimization process. Finally, the developed Bayesian updating approaches provide a way to make efficient use of the acquired SHM information to improve the accuracy in the performance assessment of naval ships and highway bridges

EqCo: Equivalent Rules for Self-supervised Contrastive Learning

In this paper, we propose a method, named EqCo (Equivalent Rules for Contrastive Learning), to make self-supervised learning irrelevant to the number of negative samples in InfoNCE-based contrastive learning frameworks. Inspired by the InfoMax principle, we point that the margin term in contrastive loss needs to be adaptively scaled according to the number of negative pairs in order to keep steady mutual information bound and gradient magnitude. EqCo bridges the performance gap among a wide range of negative sample sizes, so that we can use only a few negative pairs (e.g. 16 per query) to perform self-supervised contrastive training on large-scale vision datasets like ImageNet, while with almost no accuracy drop. This is quite a contrast to the widely used large batch training or memory bank mechanism in current practices. Equipped with EqCo, our simplified MoCo (SiMo) achieves comparable accuracy with MoCo v2 on ImageNet (linear evaluation protocol) while only involves 4 negative pairs per query instead of 65536, suggesting that large quantities of negative samples might not be a critical factor in InfoNCE loss

MPPNet: Multi-Frame Feature Intertwining with Proxy Points for 3D Temporal Object Detection

Accurate and reliable 3D detection is vital for many applications including autonomous driving vehicles and service robots. In this paper, we present a flexible and high-performance 3D detection framework, named MPPNet, for 3D temporal object detection with point cloud sequences. We propose a novel three-hierarchy framework with proxy points for multi-frame feature encoding and interactions to achieve better detection. The three hierarchies conduct per-frame feature encoding, short-clip feature fusion, and whole-sequence feature aggregation, respectively. To enable processing long-sequence point clouds with reasonable computational resources, intra-group feature mixing and inter-group feature attention are proposed to form the second and third feature encoding hierarchies, which are recurrently applied for aggregating multi-frame trajectory features. The proxy points not only act as consistent object representations for each frame, but also serve as the courier to facilitate feature interaction between frames. The experiments on large Waymo Open dataset show that our approach outperforms state-of-the-art methods with large margins when applied to both short (e.g., 4-frame) and long (e.g., 16-frame) point cloud sequences. Code is available at https://github.com/open-mmlab/OpenPCDet.Comment: Accepted by ECCV 202

TrajectoryFormer: 3D Object Tracking Transformer with Predictive Trajectory Hypotheses

3D multi-object tracking (MOT) is vital for many applications including autonomous driving vehicles and service robots. With the commonly used tracking-by-detection paradigm, 3D MOT has made important progress in recent years. However, these methods only use the detection boxes of the current frame to obtain trajectory-box association results, which makes it impossible for the tracker to recover objects missed by the detector. In this paper, we present TrajectoryFormer, a novel point-cloud-based 3D MOT framework. To recover the missed object by detector, we generates multiple trajectory hypotheses with hybrid candidate boxes, including temporally predicted boxes and current-frame detection boxes, for trajectory-box association. The predicted boxes can propagate object's history trajectory information to the current frame and thus the network can tolerate short-term miss detection of the tracked objects. We combine long-term object motion feature and short-term object appearance feature to create per-hypothesis feature embedding, which reduces the computational overhead for spatial-temporal encoding. Additionally, we introduce a Global-Local Interaction Module to conduct information interaction among all hypotheses and models their spatial relations, leading to accurate estimation of hypotheses. Our TrajectoryFormer achieves state-of-the-art performance on the Waymo 3D MOT benchmarks.Comment: 10 pages, 3 figure