Predicting Corrosion Damage in the Human Body Using Artificial Intelligence: In Vitro Progress and Future Applications Applications

Artificial intelligence (AI) is used in the clinic to improve patient care. While the successes illustrate the impact AI can have, few studies have led to improved clinical outcomes. A gap in translational studies, beginning at the basic science level, exists. In this review, we focus on how AI models implemented in non-orthopedic fields of corrosion science may apply to the study of orthopedic alloys. We first define and introduce fundamental AI concepts and models, as well as physiologically relevant corrosion damage modes. We then systematically review the corrosion/AI literature. Finally, we identify several AI models that may be Preprint implemented to study fretting, crevice, and pitting corrosion of titanium and cobalt chrome alloys

Novel trends on the assessment and management of maritime infrastructures: Outcomes from GIIP project

Climatic conditions, load, fatigue, aging and other factors causes a deterioration in civil infrastructures. As a consequence, repair and maintenance work actions are needed, being the former considered as more expensive than the latter ones. Indeed, an accurate method for measuring corrosion is a fundamental prerequisite for the detection of damaged areas and for planning an effective repairing of concrete maritime structures. In this article a comparation between two surrogate models, Markov Chains and Neuronal Networks, is presented and applied to predict the results of corrosion sensors of an infrastructure data set. The proposed methodology benefits from current monitoring practice and have the objective to develop a modular decision support system for the integrated asset management, taking into account operational, economic and environmental criteria. The results could contribute to the possibility of adapting these degradation models to aggressive environments and repaired structures, thus generating accurate maintenance strategies, and reducing costs. This methodology is part of the ongoing study “GIIP- Intelligent Port Infrastructure Management”

Ti-6Al-4V β Phase Selective Dissolution: In Vitro Mechanism and Prediction

Retrieval studies document Ti-6Al-4V β phase dissolution within total hip replacement systems. A gap persists in our mechanistic understanding and existing standards fail to reproduce this damage. This thesis aims to (1) elucidate the Ti-6Al-4V selective dissolution mechanism as functions of solution chemistry, electrode potential and temperature; (2) investigate the effects of adverse electrochemical conditions on additively manufactured (AM) titanium alloys and (3) apply machine learning to predict the Ti-6Al-4V dissolution state. We hypothesized that (1) cathodic activation and inflammatory species (H2O2) would degrade the Ti-6Al-4V oxide, promoting dissolution; (2) AM Ti-6Al-4V selective dissolution would occur and (3) near field electrochemical impedance spectra (nEIS) would distinguish between dissolved and polished Ti-6Al-4V, allowing for deep neural network prediction. First, we show a combinatorial effect of cathodic activation and inflammatory species, degrading the oxide film’s polarization resistance (Rp) by a factor of 105 Ωcm2 (p = 0.000) and inducing selective dissolution. Next, we establish a potential range (-0.3 V to –1 V) where inflammatory species, cathodic activation and increasing solution temperatures (24 oC to 55 oC) synergistically affect the oxide film. Then, we evaluate the effect of solution temperature on the dissolution rate, documenting a logarithmic dependence. In our second aim, we show decreased AM Ti-6Al-4V Rp when compared with AM Ti-29Nb-21Zr in H2O2. AM Ti-6Al-4V oxide degradation preceded pit nucleation in the β phase. Finally, in our third aim, we identified gaps in the application of artificial intelligence to metallic biomaterial corrosion. With an input of nEIS spectra, a deep neural network predicted the surface dissolution state with 96% accuracy. In total, these results support the inclusion of inflammatory species and cathodic activation in pre-clinical titanium devices and biomaterial testing

Super learner implementation in corrosion rate prediction

This thesis proposes a new machine learning model for predicting the corrosion rate of 3C steel in seawater. The corrosion rate of a material depends not just on the nature of the material but also on the material\u27s environmental conditions. The proposed machine learning model comes with a selection framework based on the hyperparameter optimization method and a performance evaluation metric to determine the models that qualify for further implementation in the proposed models’ ensembles architecture. The major aim of the selection framework is to select the least number of models that will fit efficiently (while already hyperparameter-optimized) into the architecture of the proposed model. Subsequently, the proposed predictive model is fitted on some portion of a dataset generated from an experiment on corrosion rate in five different seawater conditions. The remaining portion of this dataset is implemented in estimating the corrosion rate. Furthermore, the performance of the proposed models’ predictions was evaluated using three major performance evaluation metrics. These metrics were also used to evaluate the performance of two hyperparameter-optimized models (Smart Firefly Algorithm and Least Squares Support Vector Regression (SFA-LSSVR) and Support Vector Regression integrating Leave Out One Cross-Validation (SVR-LOOCV)) to facilitate their comparison with the proposed predictive model and its constituent models. The test results show that the proposed model performs slightly below the SFA-LSSVR model and above the SVR-LOOCV model by an RMSE score difference of 0.305 and RMSE score of 0.792. Despite its poor performance against the SFA-LSSVR model, the super learner model outperforms both hyperparameter-optimized models in the utilization of memory and computation time (graphically presented in this thesis)

Measurement of Reinforcement Corrosion in Concrete Adopting Ultrasonic Tests and Artificial Neural Network

Limited research has been performed in testing and measuring the reinforcement corrosion levels using non-destructive tests. This research applied ultrasonic-based non-destructive test and artificial neural network to the diagnosis and prediction of rebar’s non-uniform corrosion-induced damage within reinforced concrete members. Ultrasonic velocities were tested by applying ultrasonic to reinforced concrete prisms before and after the rebar corrosion. Input parameters including concrete strength, ultrasonic velocity, and the specimen dimension-related variable were used for the prediction of reinforcement corrosion level adopting artificial neural network models. Using totally 50 experimental observations, Radial Basis Function-based model was found with higher accuracy in predicting corrosion levels compared to Back Propagation-based model. This study leads to future research in high-accuracy non-destructive measurement of reinforcement corrosion in concrete

ARTIFICIAL INTELLIGENCE ANALYSIS IN SELECTED DATABASES: : PRACTICES, CONCEPTS AND MEANINGS

The discussions and analyzes presented in this article bring up issues related to Artificial Intelligence (AI), with the main objective of knowing practices, concepts and meanings of the Artificial Intelligence in selected articles. With the analysis or review article, we seek to understand the existence or not of the concept and definition in published works, with a view to contributing to new perspectives on the theme. It is a study developed with theoretical and methodological support in the qualitative approach based on a review research, having as a data collection device the consultation in the database: SCIENCE DIRECT, SCOPUS, WEB OF SCIENCE, SCIELO and REDALYC, in a random way which consisted of the first five articles from the descriptor “Artificial Intelligence” (AI), placed in the search for the referred databases in April 2020. The theoretical framework was produced in the light of scholars in the subject in question, such as Machado (2019); Turing (1950); CRAGLIA (2018); Kaufman (2018); Simon (1950); Newell (1958); McCarthy (1969); among others. The research presented some latent aspects of the reality of Artificial Intelligence (AI), seeking to understand the challenges that are posed to this field of knowledge, especially with regard to the practices, concepts and meaning in the selected articles

Prospect of using machine learning-based microwave nondestructive testing technique for corrosion under insulation: A review

Corrosion under insulations is described as localized corrosion that forms because of moisture penetration through the insulation materials or due to contaminants’ presence within the insulation material. The traditional non-destructive inspection techniques operating at a low frequency require removing insulation material to enable inspection, due to poor signal penetration. Several high-frequency inspection techniques such as the microwave technique have shown successful inspection in detecting the defect under insulations, without removing the insulations. However, the microwave technique faces several challenges such as poor spatial imaging, large errors in terms of defect size and depth owing to stand-off distance variations, optimal frequency point selection, and the presence of the outlier in microwave measurement data. The microwave technique in conjunction with machine learning approaches has tremendous potential and viability for assessing corrosion under insulation. This paper provides an in-depth review of non-destructive techniques for assessing corrosion under insulation, as well as the possibility of using machine learning approaches in microwave techniques in comparison to other conventional techniques

DEVELOPING HYBRID PHM MODELS FOR PIPELINE PITTING CORROSION, CONSIDERING DIFFERENT TYPES OF UNCERTAINTY AND CHANGES IN OPERATIONAL CONDITIONS

Pipelines are the most efficient and reliable way to transfer oil and gas in large quantities. Pipeline infrastructures represent a high capital investment and, if they fail, a source of environmental hazards and a potential threat to life. Among different pipeline failure mechanisms, pitting corrosion is of most concern because of the high growth rate of pits. In this dissertation two hybrid prognostics and health management (PHM) models are developed to evaluate degradation level of piggable pipelines, due to internal pitting corrosion. These models are able to incorporate multiple sensors data and physics of failure (POF) knowledge of internal pitting corrosion process. This dissertation covers both cases when in some pipeline's segments the pit density is low and in some segments it is high. In addition, it takes into account four types of uncertainty, including epistemic uncertainty, variability in the temporal aspects, spatial heterogeneity, and inspection errors. For a pipeline segment with a low pit density, a hybrid defect-based algorithm is developed to estimate probability distribution of maximum depth of each individual pit on that segment. This algorithm considers change in operational condition in internal pitting corrosion degradation modeling for the first time. In this way a two-phase similarity-based data fusion algorithm is developed to fuse POF knowledge, in-line inspection (ILI) and online inspection (OLI) data. In the first phase, a hierarchical Bayesian method based on a non-homogeneous gamma process is used to fuse POF knowledge and in-line inspection (ILI) data on multiple pits, and augmented particle filtering is used to fuse POF knowledge and online inspection (OLI) data of an active reference pit. The results are used to define a similarity index between each ILI pit and the OLI pit. In the second phase, this similarity index is used to generate dummy observations of depth for each ILI pit, based on the inspection data of the OLI pit. Those dummy observations are used in augmented particle filtering to estimate the remaining useful life (RUL) of that segment after the change in operational conditions when there is no new ILI data. For a pipeline segment with a high pit density, a hybrid population-based algorithm is developed to estimate the probability density function of maximum depth of the pit population on that segment. This algorithm eliminates the need of matching procedure that is computationally expensive and prone to error when the pit density is high. In this algorithm three types of measurement uncertainty including sizing error, probability of detection (POD), and probability of false call (POFC) are taken into account. In addition, initiation of new pits between the last ILI and a prediction time is modeled by using a homogeneous Poisson process. The non-linearity of the pitting corrosion process and the POF knowledge of this process is modeled by using a non-homogeneous gamma process. The estimation of these two algorithms are used in a series system to estimate the reliability of a long pipeline with multiple segments, when in some segments the pit density is low and in some segments it is high. The output of this research can be used to find the optimal maintenance action and time for each segment and the optimal next ILI time for the whole pipeline that eventually decreases the cost of unpredicted failures and unnecessary maintenance activities

Corrosion and biofouling of offshore wind monopile foundations

The impact of corrosion and biofouling on offshore wind turbines is considered to be a key issue in terms of operation and maintenance (O&M) which must be better addressed. Early design assumptions for monopile foundations anticipated low, uniform corrosion rates in a sealed compartment that would be completely air- and water-tight. However, operational experience has shown that in practice it is very difficult to maintain a fully sealed compartment, with seawater and oxygen ingress frequently observed within many monopiles across the industry. A key concern is that this situation may accelerate corrosion of the internal surfaces. On the external surfaces, the accumulation of biofouling is known to impede the safe transfer of technicians from vessel to transition piece (TP) and requires frequent cleaning. It is also likely to influence the dynamic behaviour of the foundation due to the added weight and the hydrodynamic loading due to thickness and surface roughness changes. There is sufficient evidence to suggest that the current offshore wind guidelines on biofouling could be improved to optimise the design margins. This thesis investigated the influence of internal monopile corrosion and external biofouling growth on the turbines at Teesside Offshore Wind Farm (owned and operated by EDF Energy). At Teesside, the primary drivers of internal monopile corrosion are identified as temperature, oxygen, pH and tidal variation. The influence of each of these parameters on the corrosion rate of monopile steel were investigated in a series of laboratory experiments and in-situ monopile trials. The experimental study was conducted at EDF laboratories in France using 186 corrosion coupons that were exposed to various treatments simulating internal monopile conditions. At Teesside, 49 coupons were suspended at various internal monopile locations across 5 foundations. In both cases, the weight loss measurement of coupons over time was used to determine the corrosion rates. Results suggest that tidal (wet/dry cycles) low pH and oxygen ingress have the greatest influence on the corrosion degradation of unprotected monopile steel. Internal tidal variations create a particularly aggressive corrosion environment. A decision tree matrix has been developed to predict corrosion rate classification (high/medium/low) under a range of environmental conditions. In parallel, a biofouling assessment was conducted at Teesside Offshore Wind Farm to determine the type and extent of marine growth on the intertidal and submerged zones of turbines. This has enabled a better understanding of the species diversity and community morphology but has also facilitated the development and testing of two sampling methodologies for the intertidal and subsea regions of offshore wind turbines; scrape sampling and remotely operated vehicle (ROV) surveying, respectively. The results of the assessment suggest a zonation pattern of marine growth with depth that is consistent with findings from other offshore wind farms and platforms. A super abundance of the non-native midge species T. japonicas at the intertidal zone has also been observed at other offshore wind farms in Belgium and Denmark, however, this is first evidence of its existence at a UK offshore wind farm. Removal of biofouling from the intertidal zones and jet-washing has now been optimised to coincide with peak settlement periods of mussels and barnacles. Image analysis and 3D mapping was conducted on the subsea ROV video footage to estimate thickness, roughness and added weight of biofouling. This research provides an initial investigation into the effects of internal corrosion and external biofouling on monopile foundations at Teesside Offshore Wind Farm. The methodologies developed for this investigation and the results are critically discussed in the context of asset life assessment and improvements are suggested in further work

Decision-Making for Utility Scale Photovoltaic Systems: Probabilistic Risk Assessment Models for Corrosion of Structural Elements and a Material Selection Approach for Polymeric Components

abstract: The solar energy sector has been growing rapidly over the past decade. Growth in renewable electricity generation using photovoltaic (PV) systems is accompanied by an increased awareness of the fault conditions developing during the operational lifetime of these systems. While the annual energy losses caused by faults in PV systems could reach up to 18.9% of their total capacity, emerging technologies and models are driving for greater efficiency to assure the reliability of a product under its actual application. The objectives of this dissertation consist of (1) reviewing the state of the art and practice of prognostics and health management for the Direct Current (DC) side of photovoltaic systems; (2) assessing the corrosion of the driven posts supporting PV structures in utility scale plants; and (3) assessing the probabilistic risk associated with the failure of polymeric materials that are used in tracker and fixed tilt systems. As photovoltaic systems age under relatively harsh and changing environmental conditions, several potential fault conditions can develop during the operational lifetime including corrosion of supporting structures and failures of polymeric materials. The ability to accurately predict the remaining useful life of photovoltaic systems is critical for plants ‘continuous operation. This research contributes to the body of knowledge of PV systems reliability by: (1) developing a meta-model of the expected service life of mounting structures; (2) creating decision frameworks and tools to support practitioners in mitigating risks; (3) and supporting material selection for fielded and future photovoltaic systems. The newly developed frameworks were validated by a global solar company.Dissertation/ThesisDoctoral Dissertation Civil and Environmental Engineering 201