Deterioration Model Research Articles

Seismic resilience of deteriorating bridges under changing climatic conditions

This study underscores the critical need to integrate changing climatic conditions into corrosion models for civil engineering infrastructures, particularly highway bridges, given the potential reduction in structural performance post-seismic events. The paper introduces a novel framework for assessing the seismic resilience of deteriorated highway bridges in the context of changing climatic conditions. The framework is demonstrated on a non-seismically designed simply supported highway bridge situated near the sea in a seismically active region of Gujarat, India. An improved corrosion deterioration model is used that considers the impact of climate change and non-uniform pitting corrosion for evaluating the deterioration of RC bridge components. A detailed three-dimensional finite-element model of the case-study bridge is developed that can accurately simulate various failure modes of corroding bridge piers. Time-varying seismic fragility curves are developed using damage limit states and probabilistic seismic demand models while considering the influence of climate change. Bridge seismic resilience is estimated by aggregating the seismic vulnerability, losses, and recovery functions. Results show that incorporation of changing climatic factors will considerably reduce the seismic resilience of the 75-year corroded bridge up to 56 %. Finally, a comparison of seismic fragility and resilience is carried out using the proposed and conventional corrosion deterioration model to evaluate the significance of considering the effects of climate change in the seismic resilience assessment framework.

A novel hybrid fuzzy analytical hierarchy process–game theory model for prioritizing factors affecting the deterioration of water pipelines

Water pipes face significant aging and degradation problems due to several pipe-related, soil-related, operational, and environmental factors. Hence, the paramount objective of this research paper is to prioritize the criticality of the factors affecting the deterioration of water pipes in Hong Kong. The framework of the developed model is envisioned based on two main modules, namely weight computation and weight aggregation. The first module incorporates identifying and categorizing water deterioration factors. Then, the relative importance priorities of water deterioration factors are scrutinized using seven weight computation methods. These methods encompass analytical hierarchy process (AHP), Monte Carlo AHP, fuzzy AHP, magnitude-based fuzzy AHP, total difference-based fuzzy AHP, spherical fuzzy AHP and Pythagorean fuzzy AHP. In this regard, fuzzy-based and Monte Carlo-based methods are leveraged to circumvent the critical shortcomings of classical AHP. The performances of weight computation methods are analyzed using statistical evaluation indicators of satisfactory index (SAT) and distance between weights (WD). The second module is a hybrid meta-heuristic-based game theory model designated for compiling the importance weights of deterioration factors obtained from the first module. In this context, a set of widely acclaimed meta-heuristics are exploited and examined for optimizing the significance of deterioration factors. Analytical results exemplified that soil-related factors implicate the deterioration process more than pipe-related, operational-related, and environmental-related factors. It was also inferred that water pressure (6.64%) is the most significant factor influencing water pipe deterioration followed by internal corrosion and protection method (6.11%), and then soil corrosivity (6.05%). On the other hand, length (1.93%), rain deficit (1.97%), and street block length (2.33%) constitute the least influencers on water pipe deterioration. Results also demonstrated that spherical FAHP outperformed other variants of AHP accomplishing SAT and WD of 0.065 and 0.057, respectively. Comparative analysis revealed that particle swarm optimization-based game theory is a better mechanism than the remainder of meta-heuristic-based game theory models in obtaining a more accurate compromised-based weighting vector to the experts’ judgments. It is envisaged that this research can assist the water supplies department in identifying, assessing, and prioritizing the impairment causes of water pipelines in Hong Kong. It can also aid in establishing more accurate deterioration models and more cost-effective maintenance intervention programs.

Predictive risk scores for visual prognosis after photodynamic therapy for central serous chorioretinopathy.

To comprehensively evaluate baseline characteristics of patients with central serous chorioretinopathy (CSC) and develop predictive risk scores to identify visual prognosis. This single-institute, retrospective cohort study included 144 eyes of 144 patients with CSC who underwent photodynamic therapy and achieved serous retinal detachment resolution. We developed and assessed the performance of several risk scores for best-corrected visual acuity (BCVA) outcomes six months post-treatment: i) BCVA improvement (≤-1.0 logMAR), and ii) BCVA deterioration (≥+ 1.0 logMAR). The BCVA improvement models used photoreceptor outer segment thickness, loss of photoreceptor outer segment, and neurosensory retinal thickness (NSRT), while the BCVA deterioration models included outer nuclear layer thickness and NSRT. The BCVA improvement models demonstrated a corrected area under the curve (AUC) of 0.786 (95% confidence interval [CI]: 0.699-0.864), with 80.4% sensitivity, and 71.2% specificity. The BCVA deterioration models achieved a corrected AUC of 0.864 (95% CI: 0.742-0.958), with 85.7% sensitivity, and 83.5% specificity. The predictive models for CSC exhibited favorable performance in predicting individual visual prognoses. A thinner outer nuclear layer may be associated with BCVA deterioration, whereas preservation of the photoreceptor outer segment may be correlated with BCVA improvement. WHAT IS KNOWN : Pre-treatment best-corrected visual acuity, thickness of each sensory retinal layer, time fromonset to treatment, and macular atrophy were each found to be associated with visualprognosis for patients with central serous chorioretinopathy (CSC). The current study comprehensively assessed potential prognostic factors and precisely identified individual likelihood of visual prognosis. The study found that different regions of the sensory retina were associated with either worsening or improving visual acuity. Accurately predicting visual outcomes after photodynamic therapy for CSC would help healthcare providers create personalized treatment plans and enable patients to make informed decisions about their treatment based on their expected visual results.

A data-driven framework for identifying patient subgroups on which an AI/machine learning model may underperform

A fundamental goal of evaluating the performance of a clinical model is to ensure it performs well across a diverse intended patient population. A primary challenge is that the data used in model development and testing often consist of many overlapping, heterogeneous patient subgroups that may not be explicitly defined or labeled. While a model’s average performance on a dataset may be high, the model can have significantly lower performance for certain subgroups, which may be hard to detect. We describe an algorithmic framework for identifying subgroups with potential performance disparities (AFISP), which produces a set of interpretable phenotypes corresponding to subgroups for which the model’s performance may be relatively lower. This could allow model evaluators, including developers and users, to identify possible failure modes prior to wide-scale deployment. We illustrate the application of AFISP by applying it to a patient deterioration model to detect significant subgroup performance disparities, and show that AFISP is significantly more scalable than existing algorithmic approaches.

Quantitative Evaluation of Reinforced Concrete Slab Bridges Using a Novel Health Index and LSTM-Based Deterioration Models

The Health Index (HI) serves as an essential tool for assessing the structural and functional condition of bridges, calculated based on the condition of structural components and the serviceability of the bridge. Its primary purpose is to identify the most deteriorated structures in an asset inventory and prioritize those in most urgent need of repair. However, a frequently cited issue is the lack of accurate and objective data, with the determination of the HI often being heavily reliant on expert opinions and engineering judgment. Furthermore, the HI systems used in most countries are dependent on the current state of bridge components, making it challenging to use as a proactive indicator for factors such as the rate of bridge aging. To address this issue, this study introduces a novel HI as a quantitative evaluation metric for reinforced concrete slab bridges and details the process of deriving the HI based on deterioration models. The deterioration models are derived by preprocessing the deterioration data of reinforced concrete (RC) slab bridges, wherein the relationship between time and deterioration is directly employed for training a long short-term memory model. The HI was validated through a case study involving six RC slab bridges, wherein accuracies of >93% were achieved, confirming that the proposed quantitative evaluation methodology can significantly contribute to maintenance decisions for bridges.

Transferability and comparability of sewer deterioration models – a case study on Norwegian sewer data

ABSTRACT The adoption of emerging machine learning (ML) techniques in sewer deterioration modelling remains limited. This is primarily due to a lack of comparisons with popular models to evaluate if they perform superiorly and the extensive datasets required for ML training, which many small- to medium-sized municipalities do not possess. This study compares an emerging ML technique, random survival forest (RSF), with two established models: support vector machine (SVM) and GompitZ. Additionally, it examines the transferability of RSF models to different municipalities. The results indicate that both RSF and GompitZ have unique strengths and weaknesses, suggesting parallel use with statistical models. RSF’s performance was comparable to, or better than, SVM. Furthermore, globally trained RSF models, which use aggregated datasets of multiple municipalities, perform similarly to locally trained models, which are trained on representative municipal data. However, globally trained models are promising for municipalities lacking data or expertise to develop their own models.

Reliability-Centric Maintenance Planning for Bridge Infrastructure: A Novel Method Based on Improved Electric Fish Optimization

Bridge infrastructure provides an important effect on contemporary transportation networks, and its upkeep is significant for ensuring public safety and reducing economic impacts. Nevertheless, the aging and degradation of bridge structures present considerable challenges for asset managers, who must navigate the necessity of maintenance against constrained financial resources. Conventional maintenance approaches typically emphasize reactive repairs, which can result in elevated lifecycle expenses and risk structural integrity. This paper introduces an innovative framework aimed at optimizing bridge maintenance expenditures while maintaining structural safety. The proposed methodology incorporates a reliability-based deterioration model, an intervention effect model, a financial model, and an optimization model empowered by an Improved Electric Fish Optimization (IEFO) algorithm. The framework is demonstrated through a case study of a reinforced bridge framework designed according to the standards of Canadian highway bridge design. The findings illustrate that the proposed methodology can substantially lower lifecycle costs by investigating the most economical maintenance strategies, including minor repairs that can postpone the necessity for expensive major interventions. The optimal scenario identified by the IEFO algorithm yielded lower equivalent uniform annual costs in comparison with the traditional scenario focused solely on major repairs. This research advances the field of data-driven maintenance planning for bridge infrastructure, empowering asset managers to make well-informed decisions that effectively balance cost and safety considerations.

Dynamic Data-Driven Deterioration Model for Sugarcane Shredder Hammers Oriented to Lifetime Extension

Several sugar mills operate as waste-to-energy plants. The shredder is the initial high-energy machine in the production chain and prepares sugarcane. Its hammers, essential spare parts, require continuous replacement. Then, the search for intelligent strategies to extend the lifetime of these hammers is fundamental. This paper presents (a) a dynamic data-driven model for estimating the deterioration and predicting remaining life of the sugarcane shredder hammers during operation, for which the real data of the entering sugarcane flow and the power required to prepare the sugarcane are analyzed, and (b) a management architecture intended for online decision-making assistance to extend the hammers’ life by making a trade-off between the desired lifetime, along with a nominal shredder work satisfaction criterion. The deterioration model is validated with real data achieving an accuracy of 84.41%. The remaining life prognostic is within a confidence zone calculated from the historical sugarcane flow, with a probability close to 99%, fitting a lognormal probability distribution. A numerical example is also provided to illustrate a closed loop control, where the proposed architecture is used to extend the useful life of the hammers during operation, adjusting the incoming sugarcane flow while maintaining the nominal work satisfaction of the shredder.

Light-duty gasoline vehicle emission deterioration insights from large-scale inspection/maintenance data: The synergistic impact of usage characteristics

Accurately estimating vehicle emissions is crucial for effective air quality management. As key data for emission inventory construction, emission factors (EFs) are influenced by vehicle usage characteristics and experience deterioration. Current deterioration models often employ single-factor approaches based on vehicle age or accumulated mileage, which fail to capture the effects of varying usage intensities within the same mileage or age intervals. This study addressed this limitation by developing a novel emission deterioration model that incorporates multi-dimensional usage characteristics and that utilizes a large-scale inspection and maintenance (I/M) dataset for light-duty gasoline vehicles (LDGVs). The modeling results reveal distinct deterioration patterns for different pollutants and highlight the synergistic effects of the usage duration and intensity: natural aging significantly impacts HC and NOx emissions, while CO emissions are more strongly affected by intensive use. Specifically, China V LDGVs that were driven 4 × 104 km/yr exhibited HC, CO, and NOx deterioration rates per mile that were approximately 4.1 % lower, 10.3 % higher, and 1.1 % higher, respectively, than those of vehicles driven 2 × 104 km/yr as the mileage increased from 5 × 104 km to 10 × 104 km. By leveraging timely emission data and explicitly accounting for usage intensity, this study corrected biases in local emission estimates by 5–85 % with respect to estimates from commonly used models. This framework enables the development of more effective control strategies and refinements to policy evaluations in megacities with I/M programs.

Structure Deterioration Identification and Model Updating for Prestressed Concrete Bridges Based on Massive Point Cloud Data

As a critical component of the transportation system, the safety of bridges is directly related to public safety and the smooth flow of traffic. This study addresses the aforementioned issues by focusing on the identification of bridge structure deterioration and the updating of finite element models, proposing a systematic research framework. First, this study presents a preprocessing method for bridge point cloud data and determines the parameter ranges for key algorithms through parameter tuning. Subsequently, based on the massive point cloud data, this research explores and optimizes the methods for identifying bridge cracks and spatial deformations, significantly enhancing the accuracy and efficiency of identification. On this basis, the particle swarm optimization algorithm is employed to optimize the key parameters in crack detection, ensuring the reliability and precision of the algorithm. Additionally, the study summarizes the methods for detecting bridge structural deformations based on point cloud data and establishes a framework for updating the bridge model. Finally, by integrating the results of bridge crack and deformation detection and combining Bayesian model correction and adaptive nested sampling methods, this research sets up the process for updating finite element model parameters and applies it to the analysis of actual bridge point cloud data.

A Comprehensive Review of the Key Deterioration Factors of Concrete Bridge Decks

Bridges are generally acknowledged as one of the vital structures of transportation systems. Meanwhile, they are prone to time-variant damage and deterioration mechanisms over their life span. With that in mind, this research study aims to explore state-of-the-art work in relation to deterioration models and related critical factors of reinforced concrete bridges. Particularly, this study presents a mixed review methodology (scientometric and systematic) that reviews over 300 publications in Scopus and Web of Science databases over the period 1985–2023. The study scrutinized and categorized the wide spectrum of deterioration factors in reinforced concrete bridges with the help of deterioration models. Results manifested that implicating deterioration factors can be grouped into seven main clusters, namely chemical, material properties, design & construction, physical, operational, environmental, and force majeure. In addition, it is noted that hitherto, there has been a lack of sufficient research efforts on non-destructive evaluation-based deterioration models.

Deformation behaviour of strain-softening rock mass in tunnels considering deterioration model of elastic modulus

By analyzing the composite effects of the plastic shear strain and confining stress according to the laboratory test results, a model adopting a deteriorating elastic modulus for the intact rock is proposed. Soft rock such as coal and the strong rock such as granite with different quality are investigated. Geological Strength Index (GSI) is adopted to represent the integrity of rock, to be specific, from intact rock to the rock mass. Specifically, for the strain-softening rock mass, the elastic modulus is gained by introducing GSI into the deterioration model. The strength parameters are obtained by fitting GSI into Hoek-Brown failure criterion. Then, the elastic modulus and strength parameters of the rock mass are employed in the numerical procedure for solving out the rock deformation, the radii of the plastic softening and residual zones of the tunnels. The rationality of the numerical procedure is verified with the existing semi-analytical and analytical procedures. In the discussion, the influences of the critical plastic parameters and GSI, on the elastic modulus, rock deformation, radii of the plastic softening and residual zones are investigated. Five models for the variation of the modulus including the proposed deterioration model are defined. The rock mass deformation behaviours of the five models are compared. The results indicate that, in the plastic softening zone that is far away from the tunnel periphery, the elastic modulus is more sensitive to the plastic shear strain, whereas near the tunnel periphery, the elastic modulus is primarily affected by the confining stress. Regarding a linear decrease of the elastic modulus versus the plastic shear strain overestimates the elastic modulus to some extent, especially for the soft rock mass.

Study on resistance of basalt fiber reinforced concrete to sulfate erosion after cryogenic freeze-thaw cycles

This study aims to investigate the mechanical properties of concrete structures, such as liquefied natural gas (LNG) storage tanks, subjected to the effects of cryogenic freeze-thaw cycles and erosive environmental conditions. Mechanical properties of basalt fiber reinforced concrete (BFRC) with normal temperature (25 °C) and different temperature gradients (25 °C ∼ -80 °C, 25 °C ∼ -160 °C), fiber content (0 %, 0.1 %, 0.2 %), and sulfate erosion days (0, 60, 120) were determined. The morphology of the eroded specimens was examined, and the microstructural characteristics were investigated by scanning electron microscope (SEM). The findings revealed that the introduction of basalt fiber (BF) in the concrete significantly changes the failure mode following sulfate erosion, causing specimens to fail by ductile failure rather than brittle failure. Moreover, the mechanical properties of concrete specimens decreased significantly with increasing temperature gradients of freeze-thaw cycles. Under non-erosive conditions, the compressive strength of plain concrete after undergoing freeze-thaw cycles from 25 °C to -160 °C decreased by 22.4 % compared to plain concrete that did not undergo freeze-thaw cycles, and the tensile strength decreased by 32.5 %. Adding fibers increased the mechanical properties of specimens. Specially, after undergoing freeze-thaw cycles from 25 °C to -160 °C and being subjected to 120 days of sulfate erosion, compared with plain concrete, the compressive strength of specimens with 0.2 % fiber content increased by 20.6 %, the tensile strength increased by 37.8 %, while also increasing flexural toughness. In addition, the sulfate erosion days also had a considerable impact on the deterioration of the mechanical properties of specimens. Specially, after undergoing freeze-thaw cycles from 25 °C to -80 °C and being subjected to 120 days of erosion, the eroded specimen with 0.1 % fiber content, the compressive strength decreased by 10.9 %, the tensile strength decreased by 11.6 % compared to specimens that were not eroded, while also decreasing flexural toughness. Combined with the experimental results, the strength deterioration models of BFRC related to the sulfate erosion days and BF content were proposed at 25 °C, after freeze-thaw cycles from 25 °C to -80 °C, and after freeze-thaw cycles from 25 °C to -160 °C. The aim is to provide some data reference for the development and application of BFRC in cryogenic environments.

Macroscopic State-Level Analysis of Pavement Roughness Using Time–Space Econometric Modeling Methods

This paper used pavement condition data collected by the Federal Highway Administration (FHWA) between 2001 and 2006 aggregated by U.S. states to identify macroscopic factors affecting pavement roughness in time and space. To account for prior pavement conditions and preservation expenditure over time, time autocorrelation parameters were introduced in a spatial modeling scheme that accounted for spatial autocorrelation and heterogeneity. The proposed framework accommodates data aggregation in network-level pavement deterioration models. Because pavement roughness across different roadway classes is anticipated to be affected by different explanatory parameters, separate time–space models are estimated for nine roadway classes (rural interstate roads, rural collectors, urban minor arterials, urban principal arterials, and other freeways). The best model specifications revealed that different time–space models were appropriate for pavement performance modeling across the different roadway classes. Factors that were found to affect state-level pavement roughness in time and space included preservation expenditure, predominant soil type, and predominant climatic conditions. The results have the potential to assist governmental agencies in planning effectively for pavement preservation programs at a macroscopic level.

Stressors and suicidal ideation in low-income adults in Malaysia: A serial mediation analysis of social support and mental health symptoms.

Studies have documented a heightened risk of suicidal ideation in response to stressors, especially among people from socioeconomically disadvantaged backgrounds. However, the mechanisms of this association remain elusive. Drawing on the social deterioration and counteractive models, this study aims to elucidate the pathways linking stressors to suicidal ideation through serial mediation of social support and mental health symptoms in Malaysia. Data were collected from 404 low-income adults (33.2% male and 66.8% female) receiving monthly financial assistance from Malaysia's social welfare department. We employed stressor measures (i.e. financial, family and work), the Oslo Social Support Scale, the Patient Health Questionnaire and the Suicidal Behaviour Questionnaire-Revised. A total of 46.8% of participants reported mild-to-severe anxiety and depressive symptoms, with 11.1% classified as high risk for suicide. Direct and indirect effects were found. After controlling for age and gender, social support and mental health symptoms mediated the link between stressors and suicidal ideation. The serial mediation analysis indicates that stressors are connected to heightened suicidal ideation through a sequence involving insufficient social support, followed by elevated levels of mental health symptoms. Understanding the multifaceted relationships among stressors, social support, mental health symptoms and suicide ideation expands the potential for developing targeted interventions and preventive strategies tailored for vulnerable populations. Clinical work with low-income individuals may include implementing early systematic efforts to develop accessible mental health and integrated care services.

Study on the deterioration mechanism of hybrid basalt-polypropylene fiber-reinforced concrete under sulfate freeze-thaw cycles

This paper focuses on the deterioration mechanism of hybrid basalt-polypropylene fiber-reinforced concrete (HBPRC) under the coupling of sulfate freeze-thaw cycles. The changes in the macroscopic properties of HBPRC under the influence of different volume contents of basalt fiber (BF), polypropylene fiber (PF) and hybrid BF-BF were investigated. The microstructure of HBPRC was characterized using the nuclear magnetic resonance (NMR) technique, X-ray diffraction (XRD) and scanning electron microscopy (SEM), and the deterioration models of the BF- matrix interfacial transition zone (BF-ITZ) and the PF- matrix interfacial transition zone (PF-ITZ) in HBPRC were developed. The results showed that the fibers effectively reduced the cracking and macroscopic property damage induced by sulfate freeze-thaw cycles as the number of freeze-thaw cycles increased. When both BF and PF volume contents are mixed at 0.05 %, they can produce complementary effects, and their formation of a uniformly distributed three-dimensional mesh structure is more conducive to reducing the damage to the concrete, and their relative dynamic modulus of elasticity and compressive strength after 300 times of sulfate freeze-thaw cycles were increased by 67.10 % and 46.92 % compared with control concrete. At the same time, the addition of fibers resulted in a more stable evolution of the pore structure of the concrete, and the porosity of the specimens with fibers added at a volume admixture of 0.05 % and 0.1 % was reduced by 13.69–21.35 % after 300 cycles of sulfate freeze-thaw cycling, compared with control concrete. From the BF-ITZ and PF-ITZ deterioration models, it was concluded that the differences in the bonding performance of BF and PF to the concrete matrix affected the degree of frost damage to which they were subjected to coupling and the efficiency of salt solution replenishment, which in turn led to differences in the damage of each group of HBPRC. The reliability of the model was further verified by SEM observation of the morphological changes of different fiber-matrix ITZs before and after sulfate freeze-thaw cycles.

An efficient product design tool for aftertreatment system

A numerical simulation technique based on the conservation of mass and energy in the gas phase has been developed to optimize the aftertreatment system with the lowest costs. Both oxygen storage capacity model and catalyst deterioration model have been integrated into the three-way catalyst performance model. Applications have been discussed, including XY-L 1.5L I3 gasoline turbocharged direct injection (GTDI) production hybrid vehicle, BY-L 1.5L I4 GTDI hybrid vehicle, XY-L (overseas version) 1.5L I4 GTDI hybrid vehicle, and G-L7 1.5L I4 GTDI hybrid vehicle. Vehicle tests in support of the proposed model have been described. The developed model covers the complete range of the cold start, high temperature and volume flow conditions. To optimize a three-way catalyst performance, this work simulated the effect of air fuel ratio, space velocity, temperature, biasing adjustment on the catalyst efficiency. The simulations presented the technique’s capability of well predicting emissions on fresh and full useful life aged aftertreatment systems, respectively, and carried out under transient conditions. The investigation indicated that excess fueling was used upon engine start to heat the catalyst up to its full operating temperature of greater than 350°C. The model results prompted a redesign of the I3 and I4 1.5L GTDI China 6b, Euro VId and Tier 3/SULEV30 exhaust systems over the world light vehicle test procedure (WLTP) and federal test procedure (FTP) cycles, respectively, for example, the model suggested that the latest design for the SULEV30 aftertreatment system on XY-L (overseas version) 1.5L I4 GTDI hybrid vehicle with the revised calibrations in the areas of cold-start and closed-loop fuel treated emissions of NMOG, NOx, and CO to the 70% of the SULEV30 standards with a $64 cost reduction relative to the baseline.

Network-level optimisation approach for bridge interventions scheduling

This paper introduces a novel extension of the multi-system optimisation method, known as the 3C concept, tailored for optimising budget allocation for bridge interventions at the network level. This extended methodology accounts for the interdependencies among bridges due to their spatial proximity within the network. It incorporates direct and user costs, bridge performance indicators, and a bridge deterioration model. A real-world case study involving a portfolio of 555 bridges demonstrates the practicality of the methodology, efficiently determining the optimal intervention sequence. Over an 18-year analysis period, the proposed methodology achieved a 23% reduction in total costs by combining repairs for bridges with high to severe damage and maintenance for the others. This represents a significant improvement compared to the traditional approach, used by bridge management agencies, which relies exclusively on maintenance. The optimised procedure outperforms human intuition in managing complex bridge networks, particularly over extended periods. This methodology can assist transportation agencies in implementing and exploring various scenarios by adjusting the time between consecutive interventions and budget constraints, supporting comprehensive analysis and informed decision-making.

Evaluating the importance of footloose-type failure in ice island deterioration modeling

The drift and deterioration of large and tabular icebergs, also known as “ice islands” in the Arctic, are modeled for both operational (e.g., offshore risk mitigation) and research (e.g., oceanographic impact of melt water input) purposes. In this paper, we build a theoretical argument to show that the lateral deterioration of ice islands is controlled by the rate of sidewall notch growth at the waterline, with this growth leading to the development of underwater rams and buoyancy-induced calving via the ‘footloose’ mechanism. This dominance of footloose-type lateral deterioration allows for the majority of ice island deterioration to be simulated with only three oceanic variables: wave height, wave period, and sea-surface temperature. Information regarding the size and lineage of ice islands tracked in the Canadian Ice Island Drift, Deterioration and Detection (CI2D3) Database provides opportunity to assess our theoretical work, as the database serves as a validation dataset for simulations of ice island length and area change. When simulating the length reduction over time of ice islands tracked in the CI2D3 Database, the footloose model reduced the mean error over 80 d to +277 m, compared to −1545 and −1403 m with no-melt and thermal-melt models, respectively. We also demonstrate a new approach to simulating the areal deterioration of ice islands resulting from discrete footloose calving events. The approach utilizes two parameters: the length-to-width ratio of the ice island (r) and the width of a footloose calving event relative to the ice island's length (K). With r = 1.6 and K= 0.8, the mean error in modeled area was close to zero after 20 d of simulation. A comparison of stresses associated with footloose events from a 1D-beam model and those simulated with 3D finite-element modeling showed that the 1D and 3D simulations produce broadly similar results. This supports our approaches and parameter assignments for simulating ice island length and area reduction from footloose calving. These approaches can now be incorporated into ice island deterioration models. The benefit of this incorporation will be greatest for those interested in research of longer-term impacts of ice island deterioration on ocean properties given the greater improvements to model error over periods of time that are longer than those that usually concern offshore ice management operations.

Hybrid group opportunistic maintenance strategy for offshore wind turbines considering maintenance resource allocation in harsh working environments

The intelligent maintenance of offshore wind turbines (OWTs) is vital for sustainable energy generation and utilization, as harsh working environments generally limit their application. As offshore wind farms expand into deeper waters, operational and maintenance costs continue to increase. Therefore, devising targeted maintenance strategies for offshore wind farms is crucial. Existing strategies often overlook the role of maintenance resources in controlling the operational and maintenance costs. To address this problem, this study proposes a hybrid group opportunistic maintenance strategy to implement resource allocation-based maintenance for OWTs under dynamic conditions. First, a failure rate model based on the life decline and system coupling is proposed to capture the evolution of failures in OWTs in harsh working environments, thereby enhancing the accuracy of fault maintenance. Subsequently, a hybrid linear and nonlinear deterioration model is proposed to identify the different deterioration trends owing to the various sensitivities of diverse components to harsh working environments, which enhances the precision of degradation maintenance. In addition, a maintenance resource allocation model is proposed to allocate resources in advance, thereby increasing resource utilization and reducing maintenance costs. A multi-objective optimization approach is proposed to maximize the mean maintenance level (MML) and minimize the per unit cycle expected maintenance cost rate (PECR) by considering harsh working environments. Finally, the nutcracker optimization algorithm is introduced into the maintenance model to generate the optimal solution in a multi-objective manner. The results show that the maintenance model is feasible and effective for practical adaptive maintenance applications of OWTs in harsh working environments in practical applications.