Archives of Computational Methods in Engineering最新文献

One of the serious issues of traditional signal processing techniques in analyzing the responses of real-life structures is related to the presentation of fundamental information of nonlinear, non-stationary, and noisy signals with closely-spaced frequencies. To overcome this difficulty, numerous studies have been carried out recently to explore proper time-frequency signal processing techniques to efficiently present high-resolution representations for nonlinear characteristics of analyzed signals. Despite existing extensive reviews on vibration-based signal processing techniques in time and frequency domains for Structural Health Monitoring purposes, there exists no study in categorizing the signal processing techniques based on the feature extraction with time-frequency representations. To fill this gap, this paper presents a comprehensive state-of-the-art review on the applications of time-frequency signal processing techniques for damage detection, localization, and quantification in various structural systems. The progressive trend of time-frequency analysis methods is reviewed by summarizing their advantages and disadvantages, as well as recommendations of combination methods to be utilized for different applications in various complicated structural and mechanical systems.

Today, the majority of the leading power companies place a significant emphasis on forecasting the electricity load in the balance of power and administration. Meanwhile, since electricity is an integral component of every person’s contemporary life, energy load forecasting is necessary to afford the energy demand required. The expansion of the electrical infrastructure is a key factor in increasing sustainable economic growth, and the planning and control of the utility power system rely on accurate load forecasting. Due to uncertainty in energy utilization, forecasting is turning into a complex task, and it makes an impact on applications that include energy scheduling and management, price forecasting, etc. The statistical methods involving time series for regression analysis and machine learning techniques have been used in energy load forecasting extensively over the last few decades to precisely predict future energy demands. However, they have some drawbacks with limited model flexibility, generalization, and overfitting. Deep learning addresses the issues of handling unstructured and unlabeled data, automatic feature learning, non-linear model flexibility, the ability to handle high-dimensional data, and simultaneous computation using GPUs efficiently. This paper investigates factors influencing energy load forecasting, then discusses the most commonly used deep learning approaches in energy load forecasting, as well as evaluation metrics to evaluate the performance of the model, followed by bio-inspired algorithms to optimize the model, and other advanced technologies for energy load forecasting. This study discusses the research findings, challenges, and opportunities in energy load forecasting.

The current global demand for renewable hydrogen is increasing due to the pressing need to address climate change and transition to sustainable energy sources. In this context, fluidized bed gasification is becoming more important as a versatile technology that shows promise for hydrogen production. Its high efficiency in converting solid fuels into syngas, a precursor to hydrogen, makes it a crucial player in the search for renewable energy solutions. This review aims to explain the crucial role of hydrodynamic parameters in optimizing fluidized bed gasification for enhanced hydrogen production. The objective is to thoroughly examine and synthesize existing research on hydrodynamic parameters in fluidized bed gasification, with a focus on their significant impact on renewable hydrogen production. By carefully analyzing the complex interactions of these variables, we aim to provide valuable insights that can guide the optimization of fluidized bed gasifiers toward increased hydrogen yields and improved quality.

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Crowd detection and counting are important tasks in several applications of crowd analysis including traffic management, public safety and event planning. Automatic crowd counting using images and videos is an intriguing but complex issue that has generated considerable interest in computer vision. During the past several years, various learning models have been developed by considering several factors such as model design, input pathways, learning paradigms, computing complexity and accuracy that increases cutting-edge performance. In this work, the most critical advances in the crowd analysis field are reviewed methodically and thoroughly. Numerous crowd counting models have been arranged according to how well these models perform on different datasets using various learning approaches and evaluation metrics like mean average error and mean square error. This work provides insight into the effectiveness of different learning models for crowd analysis. It will be helpful for researchers and practitioners in choosing the appropriate model for their specific applications.

There is a chromosomal defect that significantly affects an individual’s life is Down syndrome. Early identification of Down syndrome is crucial for an accurate assessment of the fetus. The process of assess the fetus includes measurement of the crown rump length, fetal heart rate, short arm or thighs bones length, nasal bone present or absent and the thickness of fluid behind neck. And the process are done during first and second trimester of pregnancy. Various invasive and noninvasive screenings are used for Down syndrome diagnosis. Research on diagnosing Down syndrome has been extensively documented. Additionally, this survey includes various techniques using deep learning for detecting the availability of Down Syndrome and does analysis of image processing methods and formulas for its diagnosis.

Several probabilistic liquefaction triggering approaches, or liquefaction manifestation severity approaches, have been developed to consider the uncertainties related to liquefaction and its manifestations. Probabilistic approaches are essential for vulnerability and risk models that considers the consequences of liquefaction on building performance. They may be incorporated into a performance-based earthquake engineering framework through a fully probabilistic liquefaction hazard assessment. The objective is to effectively incorporate spatial interaction of two concurrent hazards, specifically earthquake-induced shaking, and liquefaction, and to develop a robust multi-hazard framework applicable to regions with limited input data. For this purpose, it is necessary to establish, according to the available probabilistic liquefaction triggering or manifestation severity assessment approaches, which set of approaches aligns optimally with vulnerability and risk models. Thus, this paper discusses the current methodologies on the ongoing probabilistic liquefaction hazard assessment approaches with the aim of defining a reliable model specific for areas with a non-liquefiable surface layer over a liquefiable layer.

This paper provides a comprehensive review on the effect of load inclination and eccentricity on the bearing capacity of shallow foundations. Regarding load eccentricity, Meyerhof’s intuitive formula ({B}{prime}=B-2{e}_{b}) aligns well with finite element analyses, though it is slightly conservative. Analysis using finite element results revealed the more accurate formula (B-1.9{e}_{b}). Concerning load inclination factors, numerous such factors exist in the literature. However, most are either intuitive or derived from small-scale experimental results, rendering them unreliable due to the significant impact of model scale on the bearing capacity of footings. Based on numerical results, it is proposed that all inclination factors (namely ({i}_{c}), ({i}_{gamma }) and ({i}_{q})) can be reliably expressed by a formula of the form ({left(1-{f}_{1}cdot {tan }left({f}_{3}delta right)right)}^{{f}_{2}}), where (delta) is the inclination angle of the loading with respect to the vertical, ({f}_{1}) and ({f}_{3}) are coefficients and ({f}_{2}=3). The latter ensures smooth transition from the bearing capacity failure to the sliding failure as (delta) increases. It is also observed that many (i-) factors in the literature and various design standards employ an impermissible combination of sliding resistance at the footing-soil interface and Mohr–Coulomb bearing capacity failure under the footing. Moreover, it is shown that only the ({i}_{c}) factor depends on the angle of internal friction of soil. Finally, Vesic’s 1975 “m” interpolation formula largely falls short in accurately representing the effect of the direction of the horizontal loading.

In recent years, fiber-reinforced polymers (FRP) in reinforced concrete (RC) members have gained significant attention due to their exceptional properties, including lightweight construction, high specific strength, and stiffness. These attributes have found application in structures, infrastructures, wind power equipment, and various advanced civil products. However, the production process and the extensive testing required for assessing their suitability incur significant time and cost. The emergence of Industry 4.0 has presented opportunities to address these drawbacks by leveraging machine learning (ML) methods. ML techniques have recently been used to forecast the properties and assess the importance of process parameters for efficient structural design and their broad applications. Given their wide range of applications, this work aims to perform a comprehensive analysis of ML algorithms used for predicting the mechanical properties of FRPs. The performance evaluation of various models was discussed, and a detailed analysis of their pros and cons was provided. Finally, the limitations that currently exist in these techniques were pinpointed, and suggestions were given to improve their prediction precision suitable for evaluating the mechanical properties of FRP components.