Traditional Assessment Methods Research Articles

Heart Disease Prediction using Machine Learning

Abstract: Heart disease remains a leading cause of morbidity and mortality worldwide. Early detection and accurate prediction of heart disease risk factors are crucial for effective prevention and timely intervention. In recent years, machine learning techniques have emerged as powerful tools for predictive analytics in healthcare. This paper presents a comprehensive review of recent advances in the application of machine learning algorithms for heart disease prediction. The review begins by discussing the significance of heart disease prediction and the limitations of traditional risk assessment methods.

A Novel Method on Wound Identification using Machine Learning and Image Processing

Automated wound detection using advanced computer vision techniques has emerged as a promising avenue within the realm of healthcare. This approach offers a streamlined and accurate means of identifying a variety of acute injuries, including bruises, abrasions, and traumatic wounds. Wound identification and assessment are crucial tasks in medical practice, facilitating appropriate treatment planning and monitoring of patient recovery. Traditional methods of wound assessment often rely on visual inspection by healthcare professionals, which can be subjective and time-consuming. In recent years, advances in machine learning (ML) and image processing have offered promising avenues for automating wound identification and analysis. This paper presents a comprehensive review of existing techniques and proposes a novel approach that leverages ML algorithms and image processing methods for accurate wound identification. We discuss various challenges, such as data variability, and provide insights into future research directions to enhance the efficacy and applicability of automated wound identification systems. In this study, we delve into the application of YOLO v8, a cutting-edge real-time object detection framework, for automated wound detection across diverse clinical scenarios

Development and application of an integrated methodology for post‐disaster field investigation of debris floods

AbstractDebris floods commonly occur in steep channels with an abundant sediment supply, and they can cause significant damage, primarily due to their higher sediment concentrations and negative impacts upon bank erosion, deep scouring and aggradation. These key hazards pose challenges for traditional assessment methods, making quantification difficult. This became obvious during a recent catastrophic debris flood that occurred on July 12, 2022, in the Heishuigou catchment (102 km2), northern Sichuan Province, China. In this case study, we developed an integrated methodology for assessing this debris flood event by incorporating field surveys, hydrological and hydraulic modelling and sediment transport calculations. Detailed information such as topographic maps, photographs, deposits, grain size distributions and inundation depths was collected to analyse the material sources, validate the parameters and conduct model calculations. The peak debris flood discharge and the supra‐critical bed shear stress ratios, estimated from hydrological and dynamic models, were incorporated to analyse the debris flood's typology and characterize its destructive mechanisms. The regional frequency–volume relationship established through bedload transport calculations was calibrated using the volume of the deposits determined via field investigation. These methods not only contribute to a comprehensive understanding of debris floods but also provide valuable support for future risk assessments and mitigation designs.

Clinical research on neurological and psychiatric diagnosis and monitoring using wearable devices: A literature review

AbstractWearable devices have opened up exciting possibilities for monitoring and managing home health, particularly in the realm of neurological and psychiatric diseases. These devices capture signals related to physiological and behavioral changes, including heart rate, sleep patterns, and motor functions. Their emergence has resulted in significant advancements in the management of such conditions. Traditional clinical diagnosis and assessment methods heavily rely on patient reports and evaluations conducted by healthcare professionals, often leading to a detachment of patients from their home environment and creating additional burdens for both patients and healthcare providers. The increasing popularity of wearable devices offers a potential solution to these challenges. This review focuses on the clinical utility of wearable devices in diagnosing and monitoring neurological and psychiatric diseases. Through research findings and practical examples, we highlight the potential role of wearable devices in diagnosing and monitoring conditions such as autism spectrum disorder, depression, epilepsy, stroke prognosis, Parkinson's disease, dementia, and other diseases. Additionally, the review discusses the benefits and limitations of wearable devices in clinical applications, while highlighting the challenges they face. Finally, it provides prospects for enhancing the value of wearable devices in the monitoring and diagnosis of neurological and psychiatric diseases.

Quantitative condition assessment method for rotating machinery using fuzzy neural network

Health condition assessment of rotating machinery has been a persistent challenge. Traditional condition assessment methods often rely on single features, limiting their application to comprehensively measure the health condition of rotating machinery. This study introduced a quantitative condition assessment method for rotating machinery using fuzzy neural network (FNN). Initially, multi-domain features of signals from rotating machinery are extracted to achieve comprehensive representation of signals in the feature space. To eliminate redundant information of various features, a feature dimensionality reduction method is explored based on variance variation and stacked auto-encoder. Afterward, a normalized health indicator is constructed by integrating the optimized features through FNN, and it can indicate the current conditions of rotating machinery. Furthermore, an early anomaly alarm strategy based on 3σ criterion is designed for rotating machinery. The abnormal signal will be recognized automatically when it exceeds the predetermined thresholds. Last, the effectiveness of the proposed method is validated on IMS bearing dataset and XJTU-SY bearing dataset. The results show that the proposed method can effectively obtain the quantitative indicators that reflect the operation conditions of rotating machinery and can accurately detect the early abnormal signals.

Performance of the non-invasive point-of-care device, EzeCheck, for haemoglobin assessment in adults and children in community and institutional care settings.

Anaemia is a major public health problem, especially in resource constrained settings. Dependency on assessment of blood hemoglobin (Hgb) concentration impedes anemia detection, risk stratification and intervention. Thus, valid, frugal and scalable technologies are needed. EzeCheck is a noninvasive portable device developed in India for predicting hemoglobin levels in human beings aged 4 years and above using a finger-tip biosensor. In this assessment, we aimed to: (i) evaluate EzeCheck's performance with an automated whole blood hemato-analyzer (Sysmex XN 1000) as the gold standard, and (ii) estimate EzeCheck's agreement with Sahli's method and HemoCue (Hb-301) in real-world primary and higher care facilities. Paired assessments were done at five sites across India i.e., Bhubaneshwar, Shimla, Solan and Mashobra and Ulhasnagar. Participants across all age groups (4 years and above) were assessed. We used a range of statistical tests to evaluate the performance of EzeCheck. It was found that EzeCheck performed well across age and gender categories with convincing validity, concordance, precision and accuracy, and acceptable bias. While comparing EzeCheck with Hemato-analyzer, no statistically significant systematic bias was found. However, EzeCheck showed significant systematic bias when compared to Sahli's method and HemoCue. We concluded that EzeCheck could detect anemia (as per WHO Hgb cut-offs) in 'real-world settings' and 'across age and gender categories', with high sensitivity, specificity and accuracy, and can serve as a replacement to traditional methods of hemoglobin assessment. Further, for countries with higher prevalence of anemia where universal screening may be mandated, the positive predictive value of EzeCheck will be higher. The likelihood ratios also indicated that the device had moderate-to-good utility. EzeCheck is suitable for embedment into program and out-patient health care settings in resource constrained contexts as a spot-check hemoglobinometer.

An approach to estimate the low cycle fatigue probabilistic curves of PBF-LB/M 316L steel from small size datasets using the remora optimization algorithm

The significant dispersion in fatigue properties is a prevalent attribute observed in metals manufactured by the Additive Manufacturing (AM). To ascertain the safety of components subjected to low cycle fatigue (LCF) loadings at a temperature of 550 °C, it is necessary to carry out a fatigue reliability assessment of AM metals. Nevertheless, the number of LCF life tests is limited owing to time-consuming nature of test and high total production cost of AM materials. Such a small size dataset cannot satisfy the criteria of the traditional reliability assessment method. This work aims to develop a novel method to evaluate the LCF reliability of 316L stainless steel manufactured by laser-based powder bed fusion of metals (PBF-LB/M) technology. Firstly, the Weibull model is coupled with the Coffin-Manson-Basquin relationship in order to characterize the dispersion of life data points at various strain levels. Secondly, reliability results from a small size dataset show a maximum relative error in fatigue life of less than 25 %, which is similar to results from a large size dataset and the traditional method using fatigue data from public sources. Among this step, the remora optimization algorithm is firstly applied to calculate the characteristic parameters of newly-proposed method. Thirdly, the LCF reliability of PBF-LB/M 316L based on the small size dataset is evaluated. The N95% is equivalent to 44 % of N50%. Finally, the PBF-LB/M 316L is compared with traditional 316L considering the reliability. The LCF properties of PBF-LB/M 316L is similar to the traditional 316L at 50 % reliability, however it’s slightly worse at 95 % reliability.

Lite RGB-based measurement method for ballast fouling index prediction through subsampling

Ballasted track is the major form of freight railroads. Despite various requirements for material selection, ballast will degrade due to wear and the infiltration of fine materials. These fine particles will cause ballast fouling and degraded track performance. The fouling condition is directly related to both ballast performance and track maintenance planning. It can be quantitatively assessed using the Fouling Index (FI), calculated as the sum of the percentages of materials passing through No. 4 (4.75 mm) and No. 200 (75 µm) sieves. Traditional fouling assessment methods rely on labor-intensive sieve analysis. While non-destructive techniques such as Ground Penetration Radar, Impulse Response, Surface Wave, and SmartRock offer alternatives, they require specialized equipment and skilled technicians for accurate interpretation. Deep learning models have also been employed, trying to assess ballast fouling conditions by segmenting particles. However, these models struggle to account for fine materials. Recently, the FI was found to be linearly correlated with the variance of RGB (Red, Green, Blue) intensities in fouled ballast images, and this correlation could be used to assess ballast fouling conditions. Improving upon the previously developed FI prediction model and making it more practical, this study proposes an enhanced and lite RGB-based model that predicts the FI through a simple random subsampling approach. The optimal subsample size is determined both experimentally and analytically. Based on the results obtained from this study, the correlation between FI and the variance of RGB intensities in fouled ballast images is further confirmed. The proposed lite model can achieve the same accuracy in FI prediction but requires fewer than 1 % of the data points needed by previous methods, which demonstrates its promising potential for ballast fouling condition assessment practice.

Government's public panic emergency capacity assessment and response strategies under sudden epidemics: A fuzzy Petri net-based approach

In the post-epidemic era, public panic has emerged as a highly significant secondary disaster, necessitating an urgent enhancement of emergency management capabilities by governments at all levels. In order to ensure a robust assessment of the government's ability to manage public panic, it is crucial to effectively address the influence of uncertain and ambiguous factors associated with such scenarios. This paper proposes a governmental public panic emergency management capability assessment method based on fuzzy Petri nets. By analyzing the factors influencing public panic across the four evolutionary stages, namely gestation, outbreak, diffusion, and fading, we establish a hierarchical evaluation index system for assessing emergency management capabilities. Additionally, we develop a range of multi-scenario emergency management strategies. To address the challenges posed by uncertainty, randomness, fuzziness, and insufficient statistical data within the assessment index system, we introduce fuzzy Petri nets and fuzzy reasoning rules to evaluate the emergency management capability of the assessment system and derive the optimal emergency management strategy. According to example simulations, the effectiveness and practicality of models and rules constructed using fuzzy Petri nets are demonstrated, highlighting their superiority over traditional assessment methods. This comprehensive approach equips the government with a versatile toolkit for effectively managing public panic emergencies.

Development and Validation of a Fully Automated Tool to Quantify 3D Foot and Ankle Alignment using Weight-Bearing CT,

IntroductionFoot and ankle alignment plays a pivotal role in human gait and posture. Traditional assessment methods, relying on 2D standing radiographs, present limitations in capturing the dynamic 3D nature of foot alignment during weight-bearing and are prone to observer error. This study aims to integrate weight-bearing CT (WBCT) imaging and advanced deep learning (DL) techniques to automate and enhance quantification of the 3D foot and ankle alignment. MethodsThirty-two patients who underwent a WBCT of the foot and ankle were retrospectively included. After training and validation of a 3D nnU-Net model on 45 cases to automate the segmentation into bony models, 35 clinically relevant 3D measurements were automatically computed using a custom-made tool. Automated measurements were assessed for accuracy against manual measurements, while the latter were analyzed for inter-observer reliability. ResultsDL-segmentation results showed a mean dice coefficient of 0.95 and mean Hausdorff distance of 1.41mm. A good to excellent reliability and mean prediction error of under 2 degrees was found for all angles except the talonavicular coverage angle and distal metatarsal articular angle. ConclusionIn summary, this study introduces a fully automated framework for quantifying foot and ankle alignment, showcasing reliability comparable to current clinical practice measurements. This operator-friendly and time-efficient tool holds promise for implementation in clinical settings, benefiting both radiologists and surgeons. Future studies are encouraged to assess the tool's impact on streamlining image assessment workflows in a clinical environment.

Thermal stratification characteristics and cooling water shortage threats for reservoir–green data centers under extreme climates: A case study of a large pumped storage power station reservoir

Utilizing the thermal stratification of reservoirs to obtain cold water for cooling green data centers (GDCs) is a new mode of energy conservation and emission reduction. However, global warming is expected to alter this phenomenon in deep reservoirs and thus may affect the digitalization process. While the frequency and intensity of extreme climate (EC) events are increasing under a changing climate, the impacts of these highly destructive events on thermal stratification and the related cascading effects on GDC cold-water supplies are still unclear. A two-lateral-dimensional thermohydrodynamic model was established to determine the characteristics of reservoir thermal stratification changes based on the heat extreme experienced by the Jinshuitan Reservoir, a large, pumped storage power station (PSPS) with a GDC in southeastern China. Fourteen different PSPS inlet/outlet schemes under 2 climate scenarios were designed, and two new indexes, namely, the risk index of cold-water shortage and the index of thermal stratification, were proposed to explore the impact of extreme climates on GDC water extraction. The results showed that compared to the present climate conditions, the reservoir experienced stronger thermal stratification with a 20.6% average increase in thermal stability, a longer thermal stratification duration with a transitional trend from seasonal to permanent stratification, and a thinner cold-water layer with a 15-m average decrease. Furthermore, the risk of cold-water shortages under EC conditions was consistently greater than that under average climate (AC) conditions (traditional assessment) when the elevation of pumped storage intake/outlet was less than 145 m. According to the traditional assessment method, the intake/outlet elevation of pumped storage reservoirs resulted in a 60.27% increase in the risk of cold-water shortages during climate extremes. Furthermore, the demand for green cold-water withdrawal and optimal pumped storage power generation benefits were met when the PSPS intake/outlet elevation was 145 m. Beyond solving this specific case, this study elucidates the importance of linking the thermal stratification and data center of cold-water withdrawal to ECs. These findings provide new insights for increasing climate change resilience.

Vehicle Damage Analysis

Abstract: Vehicle damage, resulting from accidents, environmental factors, and everyday use, presents a ubiquitous challenge to vehicle owners and the broader automotive industry. An efficient and accurate vehicle damage assessment is essential for insurance claims, resale valuations, and overall vehicle safety. However, the traditional damage assessment methods often involve human subjectivity and can be time-consuming, leading to inefficiencies and customer dissatisfaction. This abstract explores the need for modernizing and automating the vehicle damage assessment process. Vehicle damage analysis plays a crucial role in accident reconstruction, insurance claims, safety assessment, and legal proceedings. This abstract provides an overview of the key aspects of vehicle damage analysis, including the methods and tools used, as well as its broader implications. Vehicle damage analysis involves the systematic examination and assessment of damage sustained by vehicles involved in accidents, incidents, or natural disasters. This process is essential for determining the cause and severity of the damage, estimating repair costs, and establishing liability in legal cases. This abstract highlights the multifaceted nature of the problem, encompassing the need for accurate and efficient damage detection, cost estimation, integration with insurance systems, and improved customer experiences. Addressing these challenges requires the application of advanced technologies such as computer vision, artificial intelligence, and machine learning, as well as a commitment to data security, privacy, and regulatory compliance.

Text Message-Based Assessment of 90-Day Modified Rankin Scale After Stroke.

The modified Rankin Scale (mRS) is commonly used to measure disability after stroke, traditionally assessed through telephone or in-person evaluation. Here, we investigated the validity of mRS assessment through an automated text messaging system based on the simplified mRS questionnaire as an alternative method to traditional methods of assessment. A total of 250 patients admitted to 3 hospitals within the University of Pennsylvania Health System with ischemic or hemorrhagic stroke were enrolled. Participants received automated text messages sent 48 hours before their outpatient appointment at about 90 days after stroke. The mRS scores were assigned on the basis of participant responses to 2 to 4 text questions eliciting yes/no responses. The mRS was then evaluated in person or by telephone interview for comparison. Responses were compared with κ. A total of 142 patients (57%) completed the study. The spontaneous response rate to text messages was 46.5% and up to 72% with an additional direct in-person or phone call reminder. Agreement was substantial (quadratic-weighted κ=0.87 [95% CI, 0.83-0.89]) between responses derived from the automated text messaging and traditional interviews. Agreement for distinguishing functional independence (mRS 0-1) from dependence (mRS 2-5) was substantial (unweighted κ=0.79 [95% CI, 0.69-0.90]). An automated text messaging system is a feasible method for remotely obtaining the mRS after stroke and a potential alternative to traditional in-person or telephone assessment. Further studies are needed to evaluate the generalizability of text message-based approaches to stroke outcome measurement.

Scholar- Study Material Application

Scholar- Study Material Application

A Comparative Analysis of Traditional and Modern Approaches to Assessment and Evaluation in Education

For tracking student progress and designing educational programs, assessment and evaluation are critical components of the process. Assessment is data collection to monitor students' development, while evaluation analyzes this data to make informed educational choices. To enhance educational results, practical and effective assessment and evaluation techniques must be applied. These strategies enable instructors to discover and customize instruction by identifying each student's strengths and limitations. This article compares classic and modern assessment and evaluation methodologies to draw implications for educational policies and practices. The research employs a systematic literature review to analyze data gathered from diverse fields. Efficiency, appropriateness, scalability, inclusivity, technology integration, and stakeholder acceptability are critical factors in comparing assessment and evaluation approaches. These parameters were used to analyze the benefits and drawbacks of each strategy. Traditional approaches, for example, have been proven insufficient in expressing individual characteristics since they are ubiquitous and uniform. Modern approaches stand out for their ability to meet student demands and adapt to diverse circumstances. The broad use of these modern procedures, however, is much more complicated than the simply adaptable traditional methods. According to the study's results, current and traditional assessment and evaluation methods have common themes such as purpose, quality assurance, involvement, ethics, and continual development. Traditional approaches, on the other hand, remain more summative, but novel methods concentrate more on student growth via formative strategies and ongoing feedback. Furthermore, novel methods provide flexibility, technology integration, and inclusion, while conventional methods may be restricted to a specific format. Modern methodologies reflect technology's growing importance in education and provide substantial opportunities to build engaging and attractive learning environments. Traditional techniques, on the other hand, emphasize academic knowledge and memory abilities while ignoring practical applications. Assessments that involve problem and project-based learning, as well as real-world situations, are prioritized in modern techniques. Consequently, integrating traditional and contemporary assessment and evaluation methodologies is recommended and will result in a more effective and inclusive educational assessment system. This research on the applicability and efficacy of assessment and evaluation methodologies provides for the exploration of new educational approaches and techniques, as well as the chance to assess current practices and make suggestions for implementation.

Effectiveness of E-Rapor Implementation: Literature Review

Implementing E-Rapor (Electronic Report) in education has become an exciting research topic that has captured the attention of numerous researchers. Based on the findings of a literature review, E-Rapor aims to bring benefits and positive impacts to the field of education, promoting its growth in the current digital era. However, researchers have also noted several limitations in using the E-Rapor-based information system, including difficulties in its application for information processing, competence planning, and data management. This research seeks to analyze gaps in the learning outcomes achieved by students when the E-Report application system is implemented compared to traditional or manual assessment methods. The data collection method employed in this study is a literature review, which involves gathering secondary data from various reputable sources. As for the research instrument used, the researchers have formulated five research questions that serve as valuable guides throughout the data collection process. The results of the discussion show that E-Reports are useful for regularly assessing student abilities by educators and school employees, allowing quick intervention when necessary, and increasing overall educational efficiency. Due to this, the author recommends further research to understand the impact of using E-Reports on learning outcomes and educational management to be comprehensive.

Deep Learning for Predictive Toxicology Assessment Early Detection of Adverse Drug Reactions.

Predicting adverse drug reactions (ADRs) early in the drug development process is crucial for ensuring drug safety and reducing costly late-stage failures. Traditional methods for toxicity assessment rely heavily on animal testing and empirical observations, which are often time-consuming, expensive, and ethically questionable. In recent years, deep learning techniques have emerged as powerful tools for predictive toxicology, offering the potential to accelerate the identification of potential ADRs while reducing reliance on animal models.This paper reviews the current state-of-the-art deep learning approaches for predictive toxicology assessment, focusing on their applications in the early detection of ADRs. [1],[2] We discuss various deep learning architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and graph neural networks (GNNs), and their utilization in analyzing diverse data types such as chemical structures, omics data, and adverse event reports.Furthermore, we examine the challenges and limitations associated with deep learning-based predictive toxicology, including data availability, model interpretability, and regulatory acceptance. We also explore ongoing efforts to address these challenges, such as the development of standardized datasets, explainable AI techniques, and collaborations between academia, industry, and regulatory agencies.Overall, this paper highlights the potential of deep learning for early detection of ADRs in drug development and underscores the need for continued research and collaboration to realize the full benefits of these techniques in ensuring drug safety and improving public health.

E058 Disease activity measurement in rheumatoid arthritis - simpler in not always better!

Abstract Background/Aims Rheumatoid arthritis is one of the commonest inflammatory arthritis (1). The traditional method of disease activity assessment would incorporate activity measures like DAS 28, CDAI and SDAI which have been validated in clinical research (2). All these indices use 28 joint assessment method. These measures use different aspects of disease activity including swollen joint count, tender joint count, patient global scores and physician global assessment scores and inflammatory markers are included in some of the measures. We conducted a small study to compare these outcome measures in patients with moderate RA (per DAS criteria) who were recruited for disease activity assessment through MSK ultrasound. Methods Of the 30 biologic naive patients recruited, 27 were included in this study. Patients with moderate disease activity (per DAS criteria) were recruited for MSK ultrasound evaluation by a single rheumatologist. Patients with a diagnosis of fibromyalgia were excluded. These patients had been reviewed by two assessors prior to USS assessment and disease activity measures updated. Patient data was collected through electronic patient records. Data was analysed through MS Excel. Results The median age was 63. There were 66% females and 34% male patients. Majority were sero positive for either RhF, Anti CCP or both (77%). Mean DAS-28 score was 4.4 and corresponding mean CDAI and SDAI scores were 22.8 and 23.5 respectively. Mean HAQ score 1.08 and median CRP was 8.6. The median composite EULAR OMERACT synovitis score was 9.18. The average physician global assessment score for disease activity before USS was 4.9/10 and it correlated well with decision to escalate treatment in 41% patients as a result of USS assessment. Conclusion With a small sample size, this project shows that simplified disease activity measures do not necessarily provide clear evidence of disease activity and are heavily influenced by patient reported outcome measures (tender joint count and global pain scores). CDAI scores can be slightly higher compared to DAS and SDAI scores for the similar patients. Physician assessment and ultrasound can provide robust evaluation of disease activity and help in decision-making. A multicentric study with a large simple size to evaluate these findings would be helpful. Judicious use of clinical evaluation and imaging can help in tailoring treatment according to patient’s clinical condition and can also save money. Disclosure H. Crawshaw: None. E. Gates: None. C. Acebes: None. M. Jamal: None.

Metaheuristic optimization based- ensemble learners for the carbonation assessment of recycled aggregate concrete

This study addresses the enhanced prevalence of carbonation, a process accelerating steel reinforcement corrosion in recycled aggregate concrete (RAC) compared to natural aggregate concrete. Traditional carbonation depth assessment methods in RAC are noted for being labor-intensive, costly, and requiring specialized expertise. There is a noted deficiency in the application of machine learning techniques for accurately predicting carbonation depth in RAC, a gap this study aims to fill. Utilizing the extreme gradient boosting (XGBoost) technique, recognized for its efficacy in ensemble machine learning, this study innovates in modeling carbonation depth in RAC. It emphasizes the criticality of hyperparameter optimization of the XGBoost algorithm for maximizing model accuracy. To achieve this, three novel metaheuristic optimization algorithms, including reptile search algorithm (RSA), Aquila optimizer (AO), and arithmetic optimization algorithm (AOA), were introduced as global optimizers for tunning the XGBoost hyperparameters. The study was underpinned by a comprehensive database compiled from extensive literature, facilitating the development of an accurate RAC carbonation depth model. Through rigorous evaluations, including sensitivity analyses, the Wilcoxon signed-rank test, and runtime comparisons, the synthesized models demonstrated exceptional accuracy, with coefficients of determination exceeding 0.95. The XGBoost-AO algorithm, in particular, showcased superior performance, with the XGBoost-RSA algorithm providing efficient predictions considering runtime. SHapley Additive exPlanations (SHAP) interpretation highlighted environmental conditions as significant carbonation depth influencers. A user-friendly graphical user interface was developed, enhancing the practical utility of the findings for predicting carbonation depth progression in RAC over time. This research significantly advances the predictive accuracy for carbonation depth in RAC, contributing to the sustainable management of concrete infrastructures and emphasizing the integration of advanced machine learning techniques with metaheuristic optimization for environmental and structural engineering advancements.

Real-time safety risk assessment of floating offshore platforms based on on-site monitoring

Real-time safety risk assessment of in-service marine engineering structures is one of the key components for the intelligent development of marine resources. Floating platforms typically have weak modes and time-varying characteristics, and hence, traditional structural damage identification and safety assessment methods cannot be directly applied to floating platforms. The objective of this study was to achieve the real-time safety risk assessment of in-service floating platforms. First, a structural damage identification method for floating platforms was proposed based on virtual loads, and the physical parameters of the platforms were obtained through inversion of measurable data. The identification result was verified based on the lengths and stiffnesses of polyester cables, which affected the dynamic response of the floating platform. The results showed that the proposed method effectively detected the parameter changes of the platform during long-term service. Then, the neural network was used to construct a load–stress surrogate model at key positions of the floating offshore platform. This model could calculate the stresses at key positions in real time, thereby evaluating the safety risks of the platform. Lastly, a real-time safety risk assessment method for floating offshore platforms was proposed, which provides a technical solution for ensuring the safety of offshore platforms.