Application of the Machine Learning Tools in the Integrity Management of Pipelines Containing Dent-gouges and Corrosions

Dent-gouges and corrosions are two of the well-known failure mechanisms that threaten the structural integrity management of oil and gas pipelines. Dent-gouges or corrosions markedly reduce the burst capacity of pipelines as a result of localized wall thickness reduction. Fitness-for-service (FFS) assessment is commonly employed to maintain the integrity of in-service pipelines containing flaws and the burst capacity evaluation is central to the FFS assessment. As the predictive accuracy of existing FFS models is generally very poor, the use of machine learning (ML) tools provides a viable option to develop burst capacity models with high accuracy. The main objective of the present thesis is to facilitate the FFS assessment of dent-gouges and corrosions based on ML tools. The first study proposes an improved burst capacity model for pipelines containing dent-gouges based on European Pipeline Research Group (EPRG) burst capacity model using full-scale burst tests by adding a correction term. The Gaussian process regression (GPR) is employed to quantify the correction term, which is a function of six non-dimensional random variables incorporating the effect of pipe and geometric properties, sizes of dent-gouges, and internal pressure loading condition. The accuracy of the improved EPRG model, i.e. EPRG-C model, is validated based on the comparison between the test and predicted burst capacities corresponding to the test data, and shown to be markedly greater than that of the EPRG model, suggesting the high effectiveness of the correction term. The second study presents a limit state-based assessment (LSBA) framework for pipelines containing dent-gouges to achieve reliability consistent outcomes. The LSBA is formulated based on the EPRG-C model proposed in the first study by assigning appropriate partial safety factors to key variables as well as the internal pressure. The calibration of partial safety factors is carried out by making the outcomes of LSBA are consistent with those of the reliability-based assessment given different pre-selected allowable failure probabilities. The failure probabilities corresponding to extensive assessment cases covering wide ranges of pipe geometric and material properties, sizes of dent-gouges and the model error are evaluated using the first-order reliability method. The validity of the calibrated partial safety factors is demonstrated using independent assessment cases and two illustrative examples. The advantages of LSBA over the deterministic assessment procedure in terms of achieving reliability-consistent assessment outcomes is further demonstrated. The third study employs a deep learning algorithm tabular generative adversarial network (TGAN) to generate synthetic burst tests by capturing the joint probability distribution based on real full-scale burst test data of corroded pipelines. Two other ML tools, random forest (RF) and extra tree (ET), are used to tune the hyper-parameters and validate the credibility of TGAN-generated data. A simple criterion is proposed to eliminate the outliers contained in the synthetic data. The results indicate that the synthetic burst test data match well with the real data, suggesting that TGAN can accurately capture the joint probability distribution of real test data and generate credible synthetic data. The fourth study develops new ML-based burst capacity models for dent-gouges with combined real and synthetic full-scale burst tests. The synthetic burst test data are generated using TGAN framework, which is proposed in the third study. The results of which are used as the basis combined with the real burst tests to develop ML burst capacity models based on three ML tools, i.e. RF, ET and GPR. The proposed models are shown to be more accurate than the models developed using real test data only. The analysis result further indicates that trained models are markedly more accurate than the semi-empirical EPRG model widely employed in the pipeline industry

Snow Coverage Prediction using Machine Learning Techniques

Snow coverage is often predicted through analysis of satellite images. Two of the most common satellites used for predictions are MODIS and Landsat. Unfortunately, snow coverage predictions are limited either by MODIS images sets' low resolution quality or Landsat dataset's low temporal frequency. In this study, we employed a set of various machine learning techniques, including multilayer perceptrons (MLP), random forest regressor (RF), and convolutional neural networks (CNN) to model the relationship between high temporal frequency of MODIS data and high spatial resolution of Landsat data. Through various experiments, we propose an improved Fractional Snow Coverage (FSC) based on relationship between RGB, lower frequency infrared channels and regional locality.Undergraduat

Integrated Reconfigurable Autonomous Architecture System

Advances in state-of-the-art architectural robotics and artificially intelligent design algorithms have the potential not only to transform how we design and build architecture, but to fundamentally change our relationship to the built environment. This system is situated within a larger body of research related to embedding autonomous agency directly into the built environment through the linkage of AI, computation, and robotics. It challenges the traditional separation between digital design and physical construction through the development of an autonomous architecture with an adaptive lifecycle. Integrated Reconfigurable Autonomous Architecture System (IRAAS) is composed of three components: 1) an interactive platform for user and environmental data input, 2) an agent-based generative space planning algorithm with deep reinforcement learning for continuous spatial adaptation, 3) a distributed robotic material system with bi-directional cyber-physical control protocols for simultaneous state alignment. The generative algorithm is a multi-agent system trained using deep reinforcement learning to learn adaptive policies for adjusting the scales, shapes, and relational organization of spatial volumes by processing changes in the environment and user requirements. The robotic material system was designed with a symbiotic relationship between active and passive modular components. Distributed robots slide their bodies on tracks built into passive blocks that enable their locomotion while utilizing a locking and unlocking system to reconfigure the assemblages they move across. The three subsystems have been developed in relation to each other to consider both the constraints of the AI-driven design algorithm and the robotic material system, enabling intelligent spatial adaptation with a continuous feedback chain

R3Det: Refined Single-Stage Detector with Feature Refinement for Rotating Object

Rotation detection is a challenging task due to the difficulties of locating the multi-angle objects and separating them effectively from the background. Though considerable progress has been made, for practical settings, there still exist challenges for rotating objects with large aspect ratio, dense distribution and category extremely imbalance. In this paper, we propose an end-to-end refined single-stage rotation detector for fast and accurate object detection by using a progressive regression approach from coarse to fine granularity. Considering the shortcoming of feature misalignment in existing refined single-stage detector, we design a feature refinement module to improve detection performance by getting more accurate features. The key idea of feature refinement module is to re-encode the position information of the current refined bounding box to the corresponding feature points through pixel-wise feature interpolation to realize feature reconstruction and alignment. For more accurate rotation estimation, an approximate SkewIoU loss is proposed to solve the problem that the calculation of SkewIoU is not derivable. Experiments on three popular remote sensing public datasets DOTA, HRSC2016, UCAS-AOD as well as one scene text dataset ICDAR2015 show the effectiveness of our approach. Tensorflow and Pytorch version codes are available at https://github.com/Thinklab-SJTU/R3Det_Tensorflow and https://github.com/SJTU-Thinklab-Det/r3det-on-mmdetection, and R3Det is also integrated in our open source rotation detection benchmark: https://github.com/yangxue0827/RotationDetection.Comment: 13 pages, 12 figures, 9 table

Tesseract: Integrated Reconfigurable Autonomous Architecture System

TESSERACT is an autonomous architecture developed through a voxel-based robotic material system that continuously reshapes communities through a socio-economic model with shifting fractional ownership. This incentivizes users to trade and share portions of physical space in realtime (Figure 1). Based on the Integrated Reconfigurable Autonomous Architecture System, TESSERACT buildings have a continuously adaptive lifecycle enabling the shifting spatial needs of communities to be negotiated through an Observe, Generate, [re]Assemble feedback loop (Figure 2). TESSERACT is implemented with three integrated components: an interactive platform, a space planning algorithm, and a distributed robotic material system

Anchor Sampling for Federated Learning with Partial Client Participation

Compared with full client participation, partial client participation is a more practical scenario in federated learning, but it may amplify some challenges in federated learning, such as data heterogeneity. The lack of inactive clients' updates in partial client participation makes it more likely for the model aggregation to deviate from the aggregation based on full client participation. Training with large batches on individual clients is proposed to address data heterogeneity in general, but their effectiveness under partial client participation is not clear. Motivated by these challenges, we propose to develop a novel federated learning framework, referred to as FedAMD, for partial client participation. The core idea is anchor sampling, which separates partial participants into anchor and miner groups. Each client in the anchor group aims at the local bullseye with the gradient computation using a large batch. Guided by the bullseyes, clients in the miner group steer multiple near-optimal local updates using small batches and update the global model. By integrating the results of the two groups, FedAMD is able to accelerate the training process and improve the model performance. Measured by $\epsilon$-approximation and compared to the state-of-the-art methods, FedAMD achieves the convergence by up to $O(1/\epsilon)$ fewer communication rounds under non-convex objectives. Empirical studies on real-world datasets validate the effectiveness of FedAMD and demonstrate the superiority of the proposed algorithm: Not only does it considerably save computation and communication costs, but also the test accuracy significantly improves.Comment: ICML 202

Validation of a guideline-based decision support system for the diagnosis of primary headache disorders based on ICHD-3 beta

BACKGROUND: China may have the largest population of headache sufferers and therefore the most serious burden of disease worldwide. However, the rate of diagnosis for headache disorders is extremely low, possibly due to the relative complexity of headache subtypes and diagnostic criteria. The use of computerized clinical decision support systems (CDSS) seems to be a better choice to solve this problem. METHODS: We developed a headache CDSS based on ICHD-3 beta and validated it in a prospective study that included 543 headache patients from the International Headache Center at the Chinese PLA General hospital, Beijing, China. RESULTS: We found that the CDSS correctly recognized 159/160 (99.4%) of migraine without aura, 36/36 (100%) of migraine with aura, 20/21 (95.2%) of chronic migraine, and 37/59 (62.7%) of probable migraine. This system also correctly identified 157/180 (87.2%) of patients with tension-type headache (TTH), of which infrequent episodic TTH was diagnosed in 12/13 (92.3%), frequent episodic TTH was diagnosed in 99/101 (98.0%), chronic TTH in 18/20 (90.0%), and probable TTH in 28/46 (60.9%). The correct diagnostic rates of cluster headache and new daily persistent headache (NDPH) were 90.0% and 100%, respectively. In addition, the system recognized 32/32 (100%) of patients with medication overuse headache. CONCLUSIONS: With high diagnostic accuracy for most of the primary and some types of secondary headaches, this system can be expected to help general practitioners at primary hospitals improve diagnostic accuracy and thereby reduce the burden of headache in China

A holistic review on fatigue properties of additively manufactured metals

Additive manufacturing (AM) technology is undergoing rapid development and emerging as an advanced technique that can fabricate complex near-net shaped and light-weight metallic parts with acceptable strength and fatigue performance. A number of studies have indicated that the strength or other mechanical properties of AM metals are comparable or even superior to that of conventionally manufactured metals, but the fatigue performance is still a thorny problem that may hinder the replacement of currently used metallic components by AM counterparts when the cyclic loading and thus fatigue failure dominates. This article reviews the state-of-art published data on the fatigue properties of AM metals, principally including $S$--$N$ data and fatigue crack growth data. The AM techniques utilized to generate samples in this review include powder bed fusion (e.g., EBM, SLM, DMLS) and directed energy deposition (e.g., LENS, WAAM). Further, the fatigue properties of AM metallic materials that involve titanium alloys, aluminum alloys, stainless steel, nickel-based alloys, magnesium alloys, and high entropy alloys, are systematically overviewed. In addition, summary figures or tables for the published data on fatigue properties are presented for the above metals, the AM techniques, and the influencing factors (manufacturing parameters, e.g., built orientation, processing parameter, and post-processing). The effects of build direction, particle, geometry, manufacturing parameters, post-processing, and heat-treatment on fatigue properties, when available, are provided and discussed. The fatigue performance and main factors affecting the fatigue behavior of AM metals are finally compared and critically analyzed, thus potentially providing valuable guidance for improving the fatigue performance of AM metals.Comment: 201 pages, 154 figure

Graphene Oxide Quantum Dots Covalently Functionalized PVDF Membrane with Significantly-Enhanced Bactericidal and Antibiofouling Performances

Covalent bonding of graphene oxide quantum dots (GOQDs) onto amino modified polyvinylidene fluoride (PVDF) membrane has generated a new type of nano-carbon functionalized membrane with significantly enhanced antibacterial and antibiofouling properties. A continuous filtration test using E. coli containing feedwater shows that the relative flux drop over GOQDs modified PVDF is 23%, which is significantly lower than those over pristine PVDF (86%) and GO-sheet modified PVDF (62%) after 10 h of filtration. The presence of GOQD coating layer effectively inactivates E. coli and S. aureus cells, and prevents the biofilm formation on the membrane surface, producing excellent antimicrobial activity and potentially antibiofouling capability, more superior than those of previously reported two-dimensional GO sheets and one-dimensional CNTs modified membranes. The distinctive antimicrobial and antibiofouling performances could be attributed to the unique structure and uniform dispersion of GOQDs, enabling the exposure of a larger fraction of active edges and facilitating the formation of oxidation stress. Furthermore, GOQDs modified membrane possesses satisfying long-term stability and durability due to the strong covalent interaction between PVDF and GOQDs. This study opens up a new synthetic avenue in the fabrication of efficient surface-functionalized polymer membranes for potential waste water treatment and biomolecules separation