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The multisource uncertainties, including material dispersion, load fluctuation and geometrical tolerance, have crucial effects on fatigue performance of turbine bladed disks. In view of the aim of this paper, it is essential to develop an advanced approach to efficiently quantify their influences and evaluate the fatigue life of turbine bladed disks.

In this study, a novel combined machine learning strategy is performed to fatigue assessment of turbine bladed disks. Proposed model consists of two modeling phases in terms of response surface method (RSM) and support vector regression (SVR), namely RSM-SVR. Two different input sets obtained from basic variables were used as the inputs of RSM, then the predicted results by RSM in first phase is used as inputs of SVR model by using a group data-handling strategy. By this way, the nonlinear flexibility of SVR inputs is improved and RSM-SVR model presents the high-tendency and efficiency characteristics.

The accuracy and tendency of the RSM-SVR model, applied to the fatigue life estimation of turbine bladed disks, are validated. The results indicate that the proposed model is capable of accurately simulating the nonlinear response of turbine bladed disks under multisource uncertainties, and SVR-RSM model provides an accurate prediction strategy compared to RSM and SVR for fatigue analysis of complex structures.

The results indicate that the proposed model is capable of accurately simulate the nonlinear response of turbine bladed disks under multisource uncertainties, and SVR-RSM model provides an accurate prediction compared to RSM and SVRE for fatigue analysis of turbine bladed disk.

Prefabricated columns connected by grouted sleeves are increasingly used in practical projects. However, seismic fragility analyses of such structures are rarely conducted. Seismic fragility analysis has an important role in seismic hazard evaluation. In this paper, the seismic fragility of sleeve connected prefabricated column is analyzed.

A model for predicting the seismic demand on sleeve connected prefabricated columns has been created by incorporating engineering demand parameters (EDP) and probabilities of seismic failure. The incremental dynamics analysis (IDA) curve clusters of this type of column were obtained using finite element analysis. The seismic fragility curve is obtained by regression of Exponential and Logical Function Model.

The IDA curve cluster gradually increased the dispersion after a peak ground acceleration (PGA) of 0.3 g was reached. For both columns, the relative displacement of the top of the column significantly changed after reaching 50 mm. The seismic fragility of the prefabricated column with the sleeve placed in the cap (SPCA) was inadequate.

The sleeve was placed in the column to overcome the seismic fragility of prefabricated columns effectively. In practical engineering, it is advisable to utilize these columns in regions susceptible to earthquakes and characterized by high seismic intensity levels in order to mitigate the risk of structural damage resulting from ground motion.

This study aims to improve the reliability of emergency safety barriers by using the subjective safety analysis based on evidential reasoning theory in order to develop on a framework for optimizing the reliability of emergency safety barriers.

The emergency event tree analysis is combined with an interval type-2 fuzzy-set and analytic hierarchy process (AHP) method. In order to the quantitative data is not available, this study based on interval type2 fuzzy set theory, trapezoidal fuzzy numbers describe the expert's imprecise uncertainty about the fuzzy failure probability of emergency safety barriers related to the liquefied petroleum gas storage prevent. Fuzzy fault tree analysis and fuzzy ordered weighted average aggregation are used to address uncertainties in emergency safety barrier reliability assessment. In addition, a critical analysis and some corrective actions are suggested to identify weak points in emergency safety barriers. Therefore, a framework decisions are proposed to optimize and improve safety barrier reliability. Decision-making in this framework uses evidential reasoning theory to identify corrective actions that can optimize reliability based on subjective safety analysis.

A real case study of a liquefied petroleum gas storage in Algeria is presented to demonstrate the effectiveness of the proposed methodology. The results show that the proposed methodology provides the possibility to evaluate the values of the fuzzy failure probability of emergency safety barriers. In addition, the fuzzy failure probabilities using the fuzzy type-2 AHP method are the most reliable and accurate. As a result, the improved fault tree analysis can estimate uncertain expert opinion weights, identify and evaluate failure probability values for critical basic event. Therefore, suggestions for corrective measures to reduce the failure probability of the fire-fighting system are provided. The obtained results show that of the ten proposed corrective actions, the corrective action “use of periodic maintenance tests” prioritizes reliability, optimization and improvement of safety procedures.

This study helps to determine the safest and most reliable corrective measures to improve the reliability of safety barriers. In addition, it also helps to protect people inside and outside the company from all kinds of major industrial accidents. Among the limitations of this study is that the cost of corrective actions is not taken into account.

Our contribution is to propose an integrated approach that uses interval type-2 fuzzy sets and AHP method and emergency event tree analysis to handle uncertainty in the failure probability assessment of emergency safety barriers. In addition, the integration of fault tree analysis and fuzzy ordered averaging aggregation helps to improve the reliability of the fire-fighting system and optimize the corrective actions that can improve the safety practices in liquefied petroleum gas storage tanks.

In the era of artificial intelligence (AI), algorithms have gained unprecedented importance. Scientific studies have shown that algorithms are frequently mentioned in papers, making mention frequency a classical indicator of their popularity and influence. However, contemporary methods for evaluating influence tend to focus solely on individual algorithms, disregarding the collective impact resulting from the interconnectedness of these algorithms, which can provide a new way to reveal their roles and importance within algorithm clusters. This paper aims to build the co-occurrence network of algorithms in the natural language processing field based on the full-text content of academic papers and analyze the academic influence of algorithms in the group based on the features of the network.

We use deep learning models to extract algorithm entities from articles and construct the whole, cumulative and annual co-occurrence networks. We first analyze the characteristics of algorithm networks and then use various centrality metrics to obtain the score and ranking of group influence for each algorithm in the whole domain and each year. Finally, we analyze the influence evolution of different representative algorithms.

The results indicate that algorithm networks also have the characteristics of complex networks, with tight connections between nodes developing over approximately four decades. For different algorithms, algorithms that are classic, high-performing and appear at the junctions of different eras can possess high popularity, control, central position and balanced influence in the network. As an algorithm gradually diminishes its sway within the group, it typically loses its core position first, followed by a dwindling association with other algorithms.

To the best of the authors’ knowledge, this paper is the first large-scale analysis of algorithm networks. The extensive temporal coverage, spanning over four decades of academic publications, ensures the depth and integrity of the network. Our results serve as a cornerstone for constructing multifaceted networks interlinking algorithms, scholars and tasks, facilitating future exploration of their scientific roles and semantic relations.

This paper aims to examine the influence of Covid-19, current war and other factors on the dynamics of real estate prices in Ukraine from 2019Q2 to 2022Q4. More specifically, the authors examine the extent of the influence of Covid-19 and war on the real estate market in Ukraine.

The authors monitor and accumulate information flows from the existing real estate market with their subsequent in-depth math-stat processing to examine dynamics and drivers of Ukrainian real estate prices evolution.

The study finds that the Ukrainian residential property market has experienced an average growing trend from June 2019 to December 2022, despite the strong influence of pandemic and war. The analysis shows that the impact of these factors varies across different regions and property types, with some areas and property types being more affected than others. The study also identifies the main drivers of the market evolution, including cost-sensitive factors such as floor level, overall area, housing conditions and geographical location.

This research is oriented to analyze evolution of residential property market in Ukraine in 2019–2022 years characterized by influence of such disturbing factors as pandemic and military actions.

Results gained are essential for any type of Ukrainian residential market analytics implementation including but not limited to investment analysis, valuation services, collateral, insurance and taxation purposes, etc. In broader sense, it can be also useful for comparison with same type market development in other geographical arears.

Initial data base collected and constantly monitored covers all different regions of the country that gives a broad view on the overall market development influenced by pandemic and war.

The lack of a reliable database of the purchase and sale of residential properties remains one of the biggest obstacles in obtaining reliable data on their market value. This considerably complicates the process of carrying out a valuation and reduces the accuracy and reliability of the results of such work. This is especially important for market which evolves in times of unrest being influenced by such strongly disturbing factors as pandemic and military actions. The originality of the study lies in the development of a complete probabilistic processing of the initial database, which provides a reliable and accurate assessment of the market evolution. The results achieved could be used by various stakeholders, such as property owners, investors, valuers, insurers, regulators and other interested customers, to make informed decisions and mitigate risks in the turbulent Ukrainian real estate market.

The purpose of this paper is to be the first empirical article to provide necessary standard deviation inputs for adoption in probabilistic prognostications of hotel revenues and expenses, i.e. prognostications that consider risk. Commonly accepted methodologies to develop hotel financial projections resulting in point estimates of upcoming performance have been perceived as egregiously insufficient because they do not consider risk in lodging investments. Previous research has recommended the use of probabilistic methodologies to address this concern, and it has been recommended that analysts use Monte Carlo simulation. This methodology requires the estimation of standard deviations of specific, future hotel revenue and expense items, and this paper provides such inputs based on a large sample of actual, recent data.

This study provides actual standard deviations using a sample of recent hotel profit and loss (P&L) statements for over 3,000 hotels (Over 19,000 P&L statements) to provide analysts with empirically-supported standard deviations that may be applied to Uniform System of Accounts for the Lodging Industry (USALI) hotel revenues and expenses in hotel financial (revenue and expense) prognostications.

Findings are presented for standard deviations based on typical line items as defined in the USALI, and these findings may be used by practitioners as inputs for hotel financial projections. Findings also include that hotel revenue items generally have higher standard deviations than expense items. Findings are presented in detail in the manuscript, including overall findings, as well as findings based on hotel class.

Rather than practitioners adopting standard deviations of hotel revenue and expense line items based on guesswork or judgment, which is the current “state of the art” in hotel financial projections, this paper provides practitioners with actual standard deviations which may be adopted in probabilistic prognostications of hotel revenues and expenses.

This paper may be the first to provide practitioners with actual standard deviations, based on typical USALI line items, for adoption in probabilistic prognostications of hotel revenues and expenses.

This study examines the effect of temperature-dependent material models for normal-strength (NSC) and high-strength concrete (HSC) on the thermal analysis of reinforced concrete (RC) walls.

The study performs an one-at-a-time (OAT) sensitivity analysis to assess the impact of variables defining the constitutive and parametric fire models on the wall's thermal response. Moreover, it extends the sensitivity analysis to a variance-based analysis to assess the effect of constitutive model type, fire model type and constitutive model uncertainty on the RC wall's thermal response variance. The study determines the wall’s thermal behaviour reliability considering the different constitutive models and their uncertainty.

It is found that the impact of the variability in concrete’s conductivity is determined by its temperature-dependent model, which differs for NSC and HSC. Therefore, more testing and improving material modelling are needed. Furthermore, the heating rate of the fire scenario is the dominant factor in deciding fire-resistance performance because it is a causal factor for spalling in HSC walls. And finally the reliability of wall's performance decreased sharply for HSC walls due to the expected spalling of the concrete and loss of cross-section integrity.

Limited studies in the current open literature quantified the impact of constitutive models on the behaviour of RC walls. No studies have examined the effect of material models' uncertainty on wall’s response reliability under fire. Furthermore, the study's results contribute to the ongoing attempts to shape performance-based structural fire engineering.

This paper aims to focus on solving the path optimization problem by modifying the probabilistic roadmap (PRM) technique as it suffers from the selection of the optimal number of nodes and deploy in free space for reliable trajectory planning.

Traditional PRM is modified by developing a decision-making strategy for the selection of optimal nodes w.r.t. the complexity of the environment and deploying the optimal number of nodes outside the closed segment. Subsequently, the generated trajectory is made smoother by implementing the modified Bezier curve technique, which selects an optimal number of control points near the sharp turns for the reliable convergence of the trajectory that reduces the sum of the robot’s turning angles.

The proposed technique is compared with state-of-the-art techniques that show the reduction of computational load by 12.46%, the number of sharp turns by 100%, the number of collisions by 100% and increase the velocity parameter by 19.91%.

The proposed adaptive technique provides a better solution for autonomous navigation of unmanned ground vehicles, transportation, warehouse applications, etc.

This paper aims to develop preliminary damage scenarios for unreinforced masonry buildings located in low to moderate seismic hazard areas in Algeria, taking into account the specific site effects.

Three soil types were considered in this analysis according to the definition of the Algerian seismic code (RPA99/2003). Peak ground acceleration values were assigned to each soil type issued from a probabilistic seismic hazard analysis (PSHA). To highlight the effect of soil conditions on the seismic vulnerability analysis of masonry buildings, a site vulnerability increment is carried out, and the macroseismic Risk-UE method has been adopted and applied by developing two main seismic scenarios according to both return periods of the PSHA, 100 and 475 years, respectively.

Based on the preliminary results of rock site condition, it can be outlined that the significant damage obtained for different earthquake scenarios discovered a substantial worldwide seismic risk to the building stock of the study area. Once the site effect is integrated into the analysis, more high values of vulnerability indexes and expected damages are obtained. Moreover, it can be concluded that soft soil (S3) is a little bit more influential than stiff soil (S2) on the final vulnerability index compared to (S1). However, the difference between the soil effect S2 and S3 on the vulnerability index can be neglected.

Researchers are encouraged to test the mechanical approaches for more detailed outcomes of a specific building analysis.

This research proves to the Algerian decision-makers that due to the site effects and the vulnerability of the masonry buildings, an urgent intervention program is required even for existing buildings located in low to moderate seismic hazard areas.

Several seismic vulnerability types of research have been conducted in Algeria for the unreinforced masonry buildings in moderate to high seismic areas in which generally the soil effect is neglected. In this context, this research paper proves that due to the site effects and the vulnerability of the masonry buildings, special attention is required even for existing buildings located in low to moderate seismic hazard areas. With this conclusion, the requirement of taking into account the soli effect in the high seismic areas is even more pronounced and should be conducted.

This study focuses on the banking systems evaluation in Portugal, Italy, Ireland, Greece and Spain (known as the PIIGS) during the financial and post-financial crisis period from 2009 to 2018.

A conditional robust nonparametric frontier analysis (order-m estimators) is used to measure banking efficiency combined with variables highlighting the effects of Non-Performing Loans. Next, a truncated regression is used to examine if institutional, macroeconomic, and financial variables affect bank performance differently. Unlike earlier studies, we use the Corruption Perception Index (CPI) as an institutional variable that affects banking sector efficiency.

This research shows that the PIIGS crisis affects each bank/country differently due to their various efficiency levels. Most of the study variables — CPI, government debt to GDP ratio, inflation, bank size — significantly affect banking efficiency measures.

The contribution of this article to the relevant banking literature is two-fold. First, it analyses the efficiency of the PIIGS banking system from 2009 to 2018, focusing on NPLs. Second, this is the first empirical study to use probabilistic frontier analysis (order-m estimators) to evaluate PIIGS banking systems.