Archives of Computational Methods in Engineering最新文献

The assessment of the seismic vulnerability of large portfolios of existing structures is the core indicator for developing reliable risk and mitigation plans at regional and urban scales. Masonry structures are widespread in different regions worldwide, and assessing their seismic vulnerability can contribute positively to the definition of large-scale seismic zoning and risk distribution. However, traditional empirical and experimental testing approaches present several drawbacks in practice, such as that they require the analysis of a large set of data with high computational effort. This poses new challenges in terms of quickly predicting the seismic vulnerability of masonry structures by reducing manual estimations in favour of efficient approaches based on machine learning and artificial intelligence. This paper innovatively combines machine learning algorithms with probabilistic seismic hazard models, considering eight characteristic factors affecting the seismic vulnerability of masonry structures, to develop an automated model for predicting the seismic vulnerability of masonry structures. In detail, using artificial intelligence and data-driven technology, data collection and analysis were performed on 2559 masonry structures and 1913,934 acceleration records monitored by 12 seismic stations in Dujiangyan (DJY) city affected by the Wenchuan earthquake in Sichuan (SC) Province, China, on May 12, 2008. Using four developed automated learning models (K-nearest neighbor (KNN), eXtreme Gradient Boosting (XGB), decision tree (DT), and random forest (RF)), confusion matrices and receiver operating curves (ROCs) were defined, with the aim of predicting the seismic vulnerability grades of masonry structures based on different intensity zones. The results of the proposed approaches, in terms of vulnerability curves, were compared with the analogous outputs obtained by adopting existing empirical approaches and applied to the collected seismic damage dataset of masonry structures. A comparison among the four algorithms and empirical models revealed that the random forest algorithm presented the best generalizability and the highest prediction accuracy.

Quantum computing, leveraging quantum phenomena like superposition and entanglement, is emerging as a transformative force in computing technology, promising unparalleled computational speed and efficiency crucial for engineering applications. This paper provides a domain-specific review tailored to computational mechanics, contextualizing quantum algorithms within the language and challenges of scientific computing—particularly for problems such as linear systems, ODEs, PDEs, and Hamiltonian simulations. We synthesize key algorithmic building blocks, including block encoding, qubitization, quantum signal processing, and amplitude amplification, and demonstrate their relevance through practical examples and annotated Qiskit code. The review uniquely bridges foundational theory and implementation by surveying the full quantum software stack, from hardware abstractions to circuit-level design. In doing so, we lower the barrier to entry for engineering researchers and identify key obstacles to adoption, such as the construction of domain-specific oracles and the current limitations of Noisy Intermediate-Scale Quantum (NISQ) hardware for large-scale mechanics problems. This paper aims to serve as both a primer and a roadmap for advancing the integration of quantum computing into computational mechanics.

Evaluations of frequency response functions (FRFs) of various linear and non-linear mechanical systems are of utmost importance. Considerations of uncertainties attract attention in the current research community, and a reasonable assessment of the variabilities of FRFs under uncertainties is a challenging task. Indeed, several critical issues emerge and prevent successful uncertainty quantifications. In linear FRFs, spurious peaks often deteriorate the quality of response estimations, which are induced by the multimodality and discontinuity. For non-linear FRFs, the multi-solution phenomenon and complex curve structures caused by turning points or bifurcations pose a great challenge to researchers, as no existing uncertainty analysis methods can effectively handle them. The current work provides an overview of the available techniques dedicated to solving these problems by elaborating on their principles, conducting a comparative analysis of their pros and cons, demonstrating benchmarks, and illustrating their applications to various linear and non-linear systems. Outlooks for future research directions are outlined by highlighting the remaining shortcomings of current methods. Hopefully, this overview will navigate readers through different approaches, guide them in choosing the correct techniques to deal with various uncertain mechanical problems, and promote promising future developments.

Diabetic retinopathy (DR) is a perilous ocular ailment that impacts individuals with diabetes. Diabetes impacts more than 60 million individuals in India. By 2030, this pervasive problem is anticipated to have increased to approximately 578 million cases. The individual continues without any symptoms related to the condition of DR until their eyesight is impacted. The effectiveness of treatment is most important when administered before the advancement of the illness. Hence, timely detection of DR is essential for its management, since it has the potential to lead to irreversible vision loss. Diabetic retinopathy (DR) is the prevailing consequence of diabetes, affecting around 3 to 4.5 million individuals in India with the potential to cause visual impairment. The therapy necessitates expensive equipment and pharmaceuticals, and the condition needs consistent monitoring from the initial prognosis until death. Furthermore, the process of visually examining fundus pictures by skilled ophthalmologists to detect structural alterations in microaneurysms, exudates, blood vessels, hemorrhages, and the macula is an extremely laborious task. It is also prone to significant fluctuation in observations made by different observers and within observations made by the same observer. Several contemporary deep learning methods are presently used to classify input images automatically. This research presents an exhaustive examination of deep learning methodologies implemented in retinal image analysis to detect and classify diabetic retinopathy (DR). The variables that may impact the efficacy of a deep learning system in identifying diabetic retinopathy (DR) are also taken into account.

This paper provides a review of various state estimation methods and their techniques in power systems. State estimation is a technique employed to ensure a reliable and accurate representation of power system parameters, such as voltage, current, phase angle, active power, and reactive power, regardless of potential measurement errors. It estimates state variables of the power system based on measurements made across the network while also ensuring that these estimates comply with the measurements. It is particularly useful for ensuring system security by identifying bad measurements, determining the line power flows among the buses, and addressing restrictions related to economic dispatch. This paper discusses the different types of state estimation based on factors such as nature, network, and type of supply. Additionally, this study conducts a comprehensive review and analysis of conventional state estimation techniques and challenges in identifying bad data and subsequently provides a brief summary of their respective merits and limitations.

Neural Networks have shown terrific results in working out several tasks accurately. Be it speech recognition, computer vision, natural language processing, or even generative intelligence, Artificial Neural Networks are ubiquitous. These clusters of neurons with intermittent connectivity are also referred to as Multilayered Perceptron (MLP). Since its inception in 1958, MLPs have served a lot to the neural networking world. It consists of a cluster of neurons assorted at different levels in the form of layers that are connected using non-linear fixed activation functions. These neurons mimic the learning capabilities of the human brain, as it learns the weights and biases annotated with the respective neurons by the process of Backpropagation. Recently in March 2024, inspired by the Kolmogorov-Arnold representation theorem, Liu et al. proposed the Kolmogorov-Arnold Networks (KANs), which grew out of an effort to have learnable, activation functions in place of the weights of an MLP. KANs possess no linear weight vectors, rather it replaces the entire weights as evident in the MLPs with univariate activation function (here., Spline). In the introductory dissemination of KANs, the authors showed better accuracy attained by the KANs over MLPs for a wide range of tasks. Following the introductory article, several advancements have been contributed in the same direction like TKAN, Wav-KAN, DeepOKAN, etc. This review aims to study these recent developments and their respective prospects. Each of these improvisations will be thoroughly examined based on their target domain and scopes of further improvisations.

Smoothed Particle Hydrodynamics (SPH), a widely-used numerical method for simulating fluid flows with complex interfaces and boundaries, has undergone decades of development. This paper reviews the efforts towards advancing high-order SPH and its applications. The fundamentals of SPH are briefly introduced, followed by an analysis of errors arising in kernel and particle approximations. The relationship between consistency and accuracy is also discussed. To achieve high-order accuracy, this paper details three approaches: correcting SPH derivative operators, constructing kernel functions and implementing spatial reconstructions in the Riemann-SPH formulation. Finally, the applications of these high-accuracy SPH methods are described to show their potential for further development.

In recent years, nanotechnology has gone through great advancement because of its number of applications in the oil and gas sector. The usage of nanoparticles (NPs) flooding is considered the most effective method for oil recovery purposes. Despite its substantial benefits, the experimental studies on Enhanced oil recovery (EOR) require a lot of time, complicated expensive resources such as advanced equipment for experimental setup, and NPs. To overcome these issues, several mathematical models have been developed to predict oil recovery factors under different reservoir conditions, which are time and cost efficient. Therefore, the aim of this study is to explore various types of NPs and their respective roles in EOR, highlighting their impact on oil recovery factors, along with thermophysical properties of nanofluids (NFs) that improve oil displacement efficiency by reducing interfacial tension (IFT), altering wettability, and increasing fluid mobility based on the latest literature review. Additionally, this paper reviews and critically analyses the latest developments in mathematical modelling and simulating NF transport in porous media (PM), focusing on Darcy’s law, NP retention, and changes in reservoir properties. One section is dedicated to deliberating the future directions in the modelling approach, like the mathematical modelling of magnetohydrodynamics (MHD), electromagnetohydronamics (EMHD) as well as hybrid nanofluids (HNFs) and integration with AI for optimization to encourage the readers to contribute further to the advancement of modelling NF-assisted EOR processes.