bluuebunny/crossref_metadata_2025 huggingface dataset:
An innovative predictive method based on supervised teacher-student learning for forecasting limited structural responses of long-span bridges from satellite images
Authors:   Politecnico di Milano, Entezami Alireza ,   Politecnico di Milano, Behkamal Bahareh ,   Politecnico di Milano, De michele Carlo ,   Politecnico di Milano, Mariani Stefano
Published: June 2024

The prediction of structural responses of large-scale civil structures offers an effective and efficient alternative to field measurement. Recently, spaceborne remote sensing technology has been increasingly adopted to monitor complex and large bridges in long-term monitoring programs by extracting structural displacements from synthetic aperture radar images. To overcome certain limitations of such images, machine learning-aided prediction of bridge displacement responses has become the best solution. For this purpose, it is necessary to consider main environmental and operational factors that influence bridge responses. Accordingly, various contact-based sensors are installed on bridges to measure influential predictors, including ambient temperature, relative humidity, wind characteristics, and traffic volume. However, in some cases, the installation of these sensors or the preparation of their data may be impractical, leading to unmeasured and unknown conditions that can affect structural responses and are not incorporated into the prediction process. To circumvent poor performance and erroneous prediction outputs, this paper proposes an innovative This method comprises two parts: an elaborate regression model acting as a 'teacher' and a simpler model serving as a 'student.' Initially, measured predictor and response data are fed into the teacher model, which is developed from a long-short-term memory neural network to reconstruct the response data. Subsequently, the residual data between the measured and reconstructed responses is computed to serve as a new predictor. This new predictor, along with the original measured response data, is then used to train a student model developed from a single-hidden-layer feedforward neural network, which finally predicts unseen response data. The effectiveness and reliability of the proposed predictive method are validated using limited information from long-span bridges. Results indicate that this method successfully predicts bridge responses using ambient temperature as the sole main predictor.
Uncertainty propagation of relevant soil and masonry parameters to design a SHM for the Baptistery of Pisa
Authors:   University of Pisa, Bartolini Giada ,   University of Pisa, Landi Filippo ,   University of Pisa, De Falco Anna ,   University of Pisa, Croce Pietro
Published: June 2024

Historic constructions are affected by several uncertainties, among which those on mechanical parameters of masonry, because of reduced knowledge given by the limitation imposed on performing extensive destructive tests not to jeopardize the structural integrity. Besides, uncertainties on soil characterization can play a significant role in the evaluation of structural response, however is often neglected.

Finite Element Models (FEM) often represent a good compromise given the possibility of reproducing the complex geometry of historical structures while requiring relatively limited material parameters from experimental tests (to feed models). However, the computational burden can become prohibitive when the evaluation of the effects of the variation of input mechanical properties for the assessment of the current behavior of the structure and potential design of adequate interventions and definition of monitoring systems requires running several analyses.

In this work, modal analyses of a monumental Medieval construction such as the Baptistery of Pisa are carried out within a probabilistic framework including material uncertainties on both mechanical parameters of soil and masonry, which are assigned suitable probability distributions based on the limited data from in-situ campaigns combined with engineering judgment.

A gPCE-based surrogate model is employed to transform the onerous numerical runs into speeded-up analytical evaluations for the computation of Sobol’ indices to assess the influence of input variability on the first ten natural frequencies of the monument at issue.

The method leads to the identification of the most relevant soil and masonry parameters and highlights which frequencies are primarily controlled by either the stiffness of the soil or that of the aboveground historical structure.

In this sense, this work provides a first clue for designing a SHM of the Baptistery of Pisa with a view to assessing minimum detectable parameter change while monitoring natural frequencies.
Design of self-healing materials for the integration of structural health monitoring (SHM^2)
Authors:   University of Wisconsin-Milwaukee, Haider Muhammad Istiaque ,   University of Wisconsin-Milwaukee, Srivastava Vaibhav ,   University of Wisconsin-Milwaukee, Rohatgi Pradeep K. ,   University of Wisconsin-Milwaukee, Salowitz Nathan P.
Published: June 2024

Integration of the complementary fields of structural health monitoring in self-healing materials (SHM2) has the potential to transform the traditional engineering concepts of fail safe and damaged tolerant design.

Structural health monitoring seeks to embed and automate sensing capabilities within structures to determine their state and to detect degradation or damage. Once degradation or damage is detected, additional decisions and potential action are required. Reducing operational envelopes can prevent further damage accumulation or catastrophic failure or repairs can be performed to correct the degradation. Typical repair requires active involvement of personnel and potentially down time.

Self-healing materials seek to avoid maintenance needs and down time through the creation of structures and materials with an imbued capability to repair damage.

Together SHM^ 2 has the potential to create a closed loop where damage is detected using embedded sensors and automated analysis that can then initiate self-healing. The highly structured and controlled nature of self-healing materials presents an opportunity to design structures that additionally support health monitoring capabilities.

This paper presents recent research and results informing the design of shape memory alloy (SMA) fiber reinforced metal matrix composites (MMCs) to optimize self-healing capabilities and structural health monitoring (SHM^2). Fiber pull tests were performed to analyze the strength of the interface between the SMA fibers and matrix allowing for sizing to complement both composite design and healing capabilities. Analysis was performed to understand complex failure mechanisms occurring at the interface between detwinning shape memory alloys and the metal matrix. And novel composite design was performed to support both the self-healing and damage detection capabilities of the material structure.
Predicting the Dynamic Behaviour of a Concrete Dam using Statistical and Machine Learning Models
Authors:   University of Porto, Pereira Sérgio ,   National Laboratory of Civil Engineering (LNEC), Mata Juan ,   University of Porto, Magalhães Filipe ,   National Laboratory of Civil Engineering (LNEC), Gomes Jorge ,   University of Porto, Cunha Álvaro
Published: June 2024

Operational Modal Analysis is a reliable methodology for the assessment of civil engineering structures, allowing for the accurate definition of their dynamic behavior. Additionally, since it does not require the use of artificial excitation, it becomes a cost-effective choice for the performance of singular tests, as well as a consistent option for the long-term continuous monitoring of structures.

Nevertheless, the modal characteristics of structures are affected by environmental and operational conditions, concealing the variations that could emerge due to abnormal behavior. With respect to concrete dams, factors such as temperature and the level of water in the reservoir exert a pronounced influence in the evolution of natural frequencies, increasing data variability and camouflaging the behavior that would be discerned under stable conditions.

In this context, the current study seeks to examine the ability of statistical and machine learning tools to mitigate the effects of external conditions on the modal properties of concrete dams, specifically natural frequencies. To achieve this objective, the efficiency of methods incorporating measurements of variables impacting the structure, such as Multiple Linear Regressions and Neural Networks, is compared to that of tools not needing these inputs, as is the case of Principal Components Analysis and the Minimum Mean Square Error estimator.

Experimental data obtained during the continuous dynamic monitoring of a concrete dam in Portugal is used as a case study.
Surrogation and Bayesian updating of a finite element model of the Leaning Tower of Pisa
Authors:   University of Pisa, Butuc Dionis ,   University of Pisa, De Falco Anna ,   University of Pisa, Landi Filippo ,   University of Pisa, Resta Carlo ,   Politecnico di Torino, Sabia Donato ,   University of Pisa, Squeglia Nunziante ,   University of Pisa, Vignali Laura
Published: June 2024

Numerical models play a crucial role in the study and understanding of cultural heritage structures, serving as valuable tools for predicting their behavior under diverse and prospective scenarios. They are however affected by various uncertainties, which impact can be mitigated through the calibration of model parameters. For heritage structures, where testing is usually restricted to the use of non-destructive techniques, and often unable to directly assess the inherent heterogeneity of the materials, a calibration approach can prove particularly useful to obtain a working model.

This work applies a Bayesian model updating procedure to material-related uncertainties affecting a recently developed finite element model of the Leaning Tower of Pisa also comprising the underlying soil layers. A general Polynomial Chaos Expansion-based surrogation of the model was employed to evaluate the sensitivity of modal properties and ease the computational burden that comes with the probabilistic framing of the updating problem. Material property distributions were then updated, taking advantage of modal data from a newly installed sensor network which allowed the most up-to-date dynamic identification of the monument.

The results represent the first probabilistic model-based assessment of material uncertainties in a three-dimensional finite element model of the Leaning Tower of Pisa. They shed some light into the value of specific modal information, while the use of analytical surrogation paves the way for the future design of a real-time updating procedure for monitoring and damage detection.
Multi-Task AI-driven undetermined blind source identification for modal identification of large scale structures
Authors:   University of Granada, García Macías Enrique ,   University of Granada, Hernández-González Israel Alejandro ,   University of Perugia, Ubertini Filippo
Published: June 2024

The effective management of aging infrastructure represents a critical concern globally, exerting profound socio-economic influences and occupying a prominent position on the political agenda. The escalating occurrences of catastrophic collapses in recent years underscore the pressing requirement for efficient Structural Health Monitoring (SHM) strategies. These strategies play a pivotal role in aiding decision-making processes related to prioritizing interventions and rehabilitation efforts. Among SHM systems, those utilizing Operational Modal Analysis (OMA) through vibration-based techniques have gained popularity due to their non-destructive nature, comprehensive damage assessment capabilities, and relative ease of automation. Despite their advantages, current OMA methods encounter significant scalability challenges, primarily attributable to extensive computational demands and the necessity for substantial expert involvement. In this context, recent advancements in the field of artificial intelligence (AI) present a promising solution to address these scalability issues, opening the door for the development of next-generation SHM systems. This study introduces an innovative Multitask Learning Deep Neural Network (MTL-DNN) model designed for rapid and automated blind source modal identification of structures. Integrating the concepts of undetermined second-order blind source identification (SOBI) into the architecture of the network, the proposed model can reveal the independent modal components present in the raw response acceleration data with minimum computational burden. The presented results demonstrate the quasi-instantaneous modal identification capabilities of the proposed architecture, enabling the estimation of more components than the number of sensors.
Disentangled representation learning with physics-informed variational autoencoder for structural health monitoring
Authors:   Delft University of Technology (TU Delft), Koune Ioannis ,   University of Cambridge, Cicirello Alice
Published: June 2024

The maintenance and replacement of aging infrastructure is becoming increasingly costly, as a large number of structures is approaching the end of its design lifetime. Labor intensive manual inspection and assessment on a large scale is often infeasible, while data-driven machine learning techniques can fail to identify relevant failure mechanisms and suffer from poor generalization to previously unseen conditions, particularly when data is sparse. We propose a multi-task learning approach based on a physics-informed variational autoencoder that leverages a dictionary of physics-based models to improve the predictive performance when limited data is available. The latent space of the autoencoder is augmented with a set of physics-based latent variables that are interpretable and allow for prior knowledge on the latent variables to be included in the autoencoder formulation. To prevent the data-driven components of the encoder and decoder from overriding the known physics, a regularization method is used to impose physical constraints on the latent space and the generative model prediction. The feasibility of the proposed approach is evaluated on a case study of quay walls in Amsterdam, demonstrating improved performance over purely data-driven methods.
Combined Passive and Active Ultrasonic Stress Wave Monitoring of Concrete Structures: An Overview of Data Analysis Techniques and Their Applications and Limitations
Authors:   Portland State University, Schumacher Thomas ,   University of Luxembourg, Bertola Numa ,   BAM Federal Institute for Materials Research and Testing, Epple Niklas ,   Ecole polytechnique fédérale de Lausanne (EPFL), Brühwiler Eugen ,   BAM Federal Institute for Materials Research and Testing, Niederleithinger Ernst
Published: June 2024

Combined passive [or acoustic emission (AE)] and active ultrasonic stress (US) wave monitoring has been shown to provide a more holistic picture of ongoing fracture processes, damage progression, as well as slowly occurring aging and degradation mechanisms in concrete structures. Traditionally, different data analysis techniques have been used to analyze the data generated from these two monitoring approaches. For AE data analysis, for instance, signal amplitudes, hit rates, source localization, and b-value analysis have been used to detect and locate cracking. On the other hand, amplitude tracking, magnitude squared coherence (MSC), and coda wave interferometry (CWI) are examples that have been employed for US data analysis. In this presentation, we explore these data analysis techniques and show where their respective applications and limitations might be. After providing an overview of the monitoring approach and the different data analysis techniques, results and observations from select laboratory experiments, as well as an in-service structure are discussed. Finally, suggestions for further work are proposed.
Sparse Tensor Decomposition of Multi-Sensory Data for Fault Localization in Rotating Machinery Health Monitoring
Authors:   University of Manchester, Wen Haobin ,   University of Manchester, Zhang Long ,   University of Manchester, Sinha Jyoti K.
Published: June 2024

Multi-sensor monitoring is prevalent in modern structural health monitoring (SHM) practice. As the number of sensors and sampling requirements increase, a monitoring sensor network can generate substantial data which are high-volume and high-dimensional, especially for large structure and machinery. In condition-based monitoring (CBM) of rotating machines, e.g., gas turbine, rotor fault diagnosis serves a significant role in the system reliability, safety, and efficiency, which helps reduce potential damage to the on-rotor structures and avoid catastrophic failures. For multi-channel vibration measurement, classical rotor diagnosis approaches typically involve data fusion techniques based on cross spectral analysis for identifying spectral correlation, e.g., cross power spectral density, and/or matrix analysis tool for dimensionality reduction, e.g., principal component analysis. The operation on vectors or matrices may limit their effectiveness for higher-order array or tensor data. To circumvent this limitation, the third order vibrational spectral tensor is generated by representing the multi-channel acceleration spectra as the three-way array. Through nonnegative Tucker decomposition (NTD), the spectral tensor is decomposed into multiple principal factors: the spectral factor, segmental factor, channel-wise factor, and the dense tensor core. The correlations across the characteristic spectral contents and the sensor channels are revealed by the factorization, which enables the diagnosis of rotor fault and facilitates fault localization by identifying the dominant channels of the characteristic spectral factor. The method is validated on an experimental rotor testbed where the faulty channel with crack, rub, or misalignment fault, is effectively localized via 4-channel vibration measurements, which presents a promising approach for multi-sensor fusion and fault diagnosis in rotating machinery health monitoring.
Railway vibration – fast physics-based models for the prediction of ground vibration and the identification of track damage
Authors:   BAM Federal Institute for Materials Research and Testing, Auersch Lutz
Published: June 2024

The following applications of machine learning/AI for railway vibration will be discussed: 1. The prediction of the wave propagation from a railway line (completely physics based for surface lines, for tunnel lines (partially) physics-based ML for surface lines). 2. The track behaviour for the emission of train-induced ground vibration (physics based for homogeneous soil, ML for layered soil). 3. Track damage detection and quantification from FRF and moving load response. 4. Bridge damage detection and localisation from modal analysis and moving load response. 5. The use of axle-box acceleration for the identification of track/sub-soil condition and bridge resonances. Prediction calculation of railway vibration usually needs time-consuming finite element, boundary element and wavenumber domain calculations. For a user-friendly prediction software, fast calculations are needed. Several time-consuming detailed calculations should be used to develop simpler and fast models. The dynamic stiffness of isolated or un-isolated railway tracks from detailed calculations with a continuous soil is approximated with the simpler Winkler soil. The vehicle-track resonance (P2 resonance) rules the effect of the mitigation measures, and it can also be used for the on-board monitoring of the track and sub-soil condition. For the identification of track damage such as gaps between sleepers, track slabs and layers, detailed models with a continuous soil have been updated to get the best fit to the measured frequency response functions from hammer tests and the deformation pattern from the moving load response. Whereas the track damage can be locally identified, this is more difficult for bridges where the modal analysis gives mainly global information. The influence lines of the inclination for statically passing vehicles (locomotive, truck, compaction ) have been used to localise bridge damage (stiffness variations). The on-board monitoring of rail bridges needs special conditions (regular trains with special speeds) to excite and measure the bridge resonance.
Harnessing the power of thermal imagery and visual inspection- a mean for reliable damage detection of wind turbine rotor blades
Authors:   BAM Federal Institute for Materials Research and Testing, Sridaran Venkat Ramanan ,   BAM Federal Institute for Materials Research and Testing, Stamm Michael ,   Oktopus GmbH, Wittmann Jost ,   Julius-Maximilians-University Würzburg, Lauterbach Helge ,   Julius-Maximilians-University Würzburg, Bleier Michael
Published: June 2024

Generation of green electricity as part of the energy transition is leading to a growing market in the wind energy sector all over the world. Maintenance and inspection are key to the reliability, safety and efficiency of wind turbines, the regular maintenance of rotor blades focuses on damage such as erosion on the leading edge of the profile, delamination and thermal cracks due to lightning strikes. To date, visual inspection by technicians (climbers) has been the state of the art and it is time consuming besides posing safety risk for themselves. Recently, drone-based inspections using visual cameras have become more common, enabling fast, reliable and cost-effective inspections. However, no internal damage to the rotor blades can be detected during such an inspection. Thermography is a recognised method for detecting damage beneath the surface of an object, which has been promoted and further developed at BAM for years. To enhance the accuracy and reliability of wind turbine blade inspection, the fusion of thermal and visual data has emerged as a promising technique. Here, a drone-based multisensory system is being presented that combines thermal imagery, high-resolution visual images and a three-dimensional image obtained from 3D Laser scanner to represent a comprehensive view of the blades' internal and external conditions. Besides this, flight data obtained from IMU, GPS and LiDAR sensors will be used for enhancing the inspection data. Thus, fusion of images enables a more detailed analysis of potential defects and this not only improves the overall effectiveness of the inspection process but also provides windmill operators a new dimensions for data analysis and decision making.
Destructive Testing of the Pinkabach Railway Bridge – Large scale Testing of Sensor Systems for Fatigue Crack Monitoring and System Identification
Authors:   Austrian Institute of Technology, Lachinger Stefan ,   Graz University of Technology, Unterweger Harald ,   TÜV Austria Services GmbH, Lackner Gerald ,   Hottinger Brüel & Kjaer Austria GmbH, Maicz Dietmar ,   FCP Fritsch, Chiari & Partner Ziviltechniker GmbH, Reimoser Theresa ,   Graz University of Technology, Lienhart Werner ,   Schimetta Consult ZT GmbH, Mack Thomas ,   ÖBB Infrastructure AG, Forstlechner Franz
Published: June 2024

The work presents experiments carried out on an out-of-service steel railway bridge, the so called Pinkabach bridge. The objective was to test advanced and novel sensor systems, i.e. Distributed Fiber Optic Sensing (DFOS) and Acoustic Emission (AE), for the monitoring of fatigue cracks, both for crack initiation and subsequent crack growth, in steel railway bridges. Also, sensor systems for overall system identification, i.e. Tiny Motion (TM), Distributed Acoustic Sensing (DAS) and dynamic response analysis, have been tested.

The Pinkabach bridge was a single span plate girder railway bridge with a span width of 21,5m that was taken out of service and transported to a secure environment that allowed for destructive testing with permanent access to the structure itself under controlled conditions. By harmonic excitation of the structure at its first resonance frequency, cyclic stress levels comparable to the stress levels during train passage were generated and it was possible to emulate the fatigue load from decades of train traffic within the limited period of the experiments. Crack initiation and growth happened at two locations of the flanges and crack growth was monitored till the crack length was half of the flange width of the main girder. To end the experiments a static load test showed the remaining capacity of the damaged structure.

A conventional sensor system was used for validation and comparison with calculated predictions based on linear fracture mechanics, used for the design of the experiments. An overview over the whole set of experiments as well as (intermediate) results and findings on the applicability of the novel sensor systems are presented in this paper.

The experiments are a collaborative work of multiple scientific and industrial partners and have been carried out as part of Area 3.1 of the COMET Project Rail4Future, funded by the Austrian Research Promotion Agency FFG.
The damage localization method in the composite panel by multifrequency guided wave signal using parallel network
Authors:   Dalian University of Technology, Tian Tong ,   Dalian University of Technology, Yang Lei ,   Beijing University of Posts and Telecommunication, Liu Wentao ,   Dalian Maritime University, Yang Zhengyan ,   Dalian University of Technology, Wu Zhanjiun
Published: June 2024

Fiber-reinforced composite materials find extensive application in the realm of aeronautics; nonetheless, during operational phases, structural integrity may be compromised due to external impacts or extreme environmental conditions. Consequently, the imperative is the necessity of vigilantly monitoring the structural state. The structural health monitoring (SHM) technology based on ultrasonic guided waves can achieve long-distance and wide-ranging damage detection utilizing a minimal number of cost-effective piezoelectric transducers. However, traditional damage identification methods necessitate the analysis of the multi-modal characteristics of guided waves and the deployment of dense sensor arrays. In this paper, we propose a network model that leverages multi-frequency guided-wave signals to achieve spatiotemporal parallelism, incorporating residual blocks and convolutional attention mechanisms (GCRA). This model accurately estimates damage locations with an $R^2$ value of 0.9147. The average error for the six randomly selected damage locations is 0.4275 cm. The results demonstrate that the GCRA facilitates the identification of damaged positions with a high degree of confidence.
An Approach for Real-Time Monitoring of Train Derailments: An Impact-Based Monitoring Method
Authors:   Beijing Jiaotong University, Wang Wenhao ,   Beijing Jiaotong University, Cheng Xiaozhou ,   Beijing Jiaotong University, Cai Guoqiang ,   Beijing University of Technology, Zhang Ye
Published: June 2024

Train derailments are severe incidents in rail transportation, posing significant risks to passenger safety and infrastructure integrity. However, the excessive lateral vibrations of the wheelset, which are a direct cause of derailments, often cannot be effectively monitored and analyzed using traditional methods. Therefore, it is crucial to develop real-time monitoring methods that can detect and prevent derailments, ensuring the safety of railway transportation.To address and prevent train derailments, this paper introduces an impact monitoring method focused on rail fasteners. This technique enables the rapid detection of single-wheel derailments from the track. By analyzing the behavior of rail fasteners under various load conditions, we have identified key parameters that can be utilized to detect potential derailment risks. The method primarily involves monitoring changes in lateral and longitudinal vibrational energy, along with the intervals between impacts on fasteners, and incorporating the vertical displacement of wheels to trigger a derailment alarm.Simulation and experimental results demonstrate that this approach can accurately identify derailment characteristics and issue timely warnings, effectively preventing and mitigating train derailments. This method offers a practical solution for enhancing rail transportation safety and contributes to the advancements in this field.
Advanced feature extraction for the characterization of impact events on composites using multiresolution wavelet-based simulations
Authors:   University of Patras, Dimitriou Dimitris ,   University of Patras, Saravanos Dimitris A.
Published: June 2024

The detection and characterization of impact events on composite structures from the induced wave responses, involves many challenges. Coexisting guided waves of high dispersive nature propagate after impact, entailing continuous time variations in their wavenumber, frequency and group velocity content, which complicate the exact estimation of the time-of-flight and the extraction of impact characteristics. A multiresolution finite wavelet domain computational method, combined with appropriate contact laws, is presented to provide enhanced simulation and characterization capabilities of impact events. An explicit multiresolution time integration scheme involves a coarse solution, followed by finer solutions that are sequentially added to the coarse one, until convergence is achieved. The hierarchical character of the approach makes the impact simulation very fast and accurate. Moreover, due to the filtering properties of Daubechies wavelet functions, each resolution component effectively models and isolates specific wavenumber spectra, providing the capability to separate coexisting wave modes, and to overcome difficulties imposed by the dispersive nature of the resultant guided waves. It is demonstrated that the multiresolution simulation can reveal wave characteristics and time-of-flight features that no other traditional single-resolution method can do, and provides the basis for the development of powerful inverse methods that can localize the impact and characterize the impactor parameters. The method is first applied on impacted composite strips and the structural responses of each resolution component are assessed in order to obtain the desired features for impact location estimation and characterization. Finally, the method is implemented on an impacted composite plate structure and the various wave characteristics simulated by the fine components are presented, evincing the advanced features that the proposed method provides.
A Case Study on Vibration Analysis using DeepONet
Authors:   University of Groningen, Liu Hailong ,   University of Ghent (UGent), Hedayatrasa Saeid ,   Queen Mary University of London, Zhu Yunpeng ,   University of Ghent (UGent), Cheng Liangliang
Published: June 2024

Structural Health Monitoring (SHM) is a crucial element in maintenance management, playing a vital role in ensuring public safety and economic sustainability. The effectiveness of SHM relies on accurately identifying and analyzing the underlying dynamic behaviors. However, the complexity of structures, material properties, and environmental factors often introduces nonlinear characteristics into the collected dynamic responses from sensing instruments, posing a challenge in accurately identifying the underlying complex (nonlinear) dynamics. Machine learning has the potential to complement physics-based methods for SHM, offering higher accuracy and other benefits. However, many resulting systems lack interpretability and trustworthiness. In addressing these challenges, this paper proposes a novel approach for identifying and analyzing underlying complex dynamics in a broad range of mechanical and civil structures. The approach is based on the recently developed Deep Operator Network (DeepONet), employed for learning nonlinear operators. In this approach, DeepONet is initially utilized to capture the fundamental operators of the dynamic system. Subsequently, these operators are optimized and reassembled based on existing physical knowledge. This strategy yields results demonstrating not only a high level of accuracy in predicting dynamic responses but also an intuitive representation of complex modes within the dynamic system. It establishes a tangible connection between the outcomes of the system response and the underlying mechanics of vibration, facilitating a more intuitive analysis of the system's vibrational properties. To validate this approach, an Euler beam model is employed, and the experimental results convincingly affirm the effectiveness of the strategy.
The Trace of Greek Wisdom in the poems of Abdul Wase Jabali Gharjestani
Authors:   دانش‌جوی‌ِ دکتری ادبیات فارسی، دانش‌کده‌/ پوهنځی ادبیات و علوم انسانی، دانش‌گاه/ پوهنتون تهران, یزدان‌پناه عبدالرحیم , مؤذنی دکتر علی محمد ,   گروه ادبیات فارسی، دانش‌کده‌/ پوهنځی ادبیات و علوم انسانی، دانش‌گاه/ پوهنتون تهران, هادی دکتر روح‌الله ,   گروه ادبیات فارسی، دانش‌کده‌/ پوهنځی ادبیات و علوم انسانی، دانش‌گاه/ پوهنتون تهران
Published: June 2024

Philosophy is the co-born of humankind and philosophizing is also a manifestation of human thinking. It must be said with certainty that no historical period has been without philosophy. There is no doubt that Western philosophy has reached us in written form from Greece and then to the whole world. At the beginning of Islam, many Greek ruling sources were translated by Islamic thinkers, and caused the emergence of various scholars in the field of Islamic philosophy and the spread of philosophical propositions among the Persian poets and writers. Abdul-Wase Jabali Gharjistani, is one of the poets of the 6th century of Hijri, while he was fully familiar with his contemporary sciences, he had a remarkable knowledge of Greek wisdom and reflected it in his poetry. In the present article, with the aim of showing the subtleties of the poet's philosophical vision, the writer is more interested in answering the question of whether it is possible to consider Jabali as a follower of Greek philosophical thought. Because as appearing from his poems, The Jabali's way of thinking in addition to Epicureanism is thinking of happiness; In general, it is similar to the ideas of Hellenism. In order to provide an answer to the above question according to the indicators and components of Greek philosophy, in an inductive way using a descriptive-analytical method, among all the poems of Divan Jabali, there are poems with a way of thinking Greek that is classified, adapted and analyzed. The result of the research shows that Jebeli's thought is in the components of the materials of existence; Epicurean philosophy or honoring the soul, seizing the opportunity, paying attention to worldly pleasures and the influence of the stars and the heavens on humans is consistent with the Greek philosophy
Analyzing the types of semantic deviance in Kalileh and Damanah Nasrallah Menshi with an emphasis on the example of Sajjoudi
Authors:   دانش‌گاه/ پوهنتون تبریز, حسن‌نژاد علی , اکرمی دکتر میرجلیل اکرمی ,   دانش‌گاه/ پوهنتون تبریز
Published: June 2024

The essential and irrefutable position of the Persian language and literature as one of the most significant and influential literatures among the world literature is a clear and obvious thing, and in the meantime, the amazing influence that the language has as the main means of communication and creation of works of art on The literature is brilliant. Poets and writers can use different tools to take advantage of the power of language in literature so that to enrich their literary works. One of the tools is the semantic avoidance. Semantic norm avoidance is one of the tools of literary creation in poetry, and its investigation in prose will lead to its manifestation and poeticization. The authors of the research, using the descriptive-analytical method and library studies, while relying on the model presented by Farzan Sajudi (1378), to categorize the types of semantic norm avoidance based on the framework proposed by Leach (1969), to define semantic norm avoidance. In terms of prostration and explanation of its types, they have presented the frequency of each of them in the book of Kalileh and Demneh. The results of this research show that in the book Kalileh and Demnah Nasrullah Munshi, semantic deviance by the method of objectification is used more and its frequency compared to other types of semantic deviance is more, and this topic can be used in stylistics studies. This great work and its difference with other works of the same period should be taken into consideration.
Interference between Punishments for Several crimes in Islamic Jurisprudence and the Afghan Penal Code
Authors:   قندهار پوهنتون, طيب پوهنمل عبدالوهاب , شيرزاد پوهنيار راز‌محمد ,   قندهار پوهنتون
Published: June 2024

It has been recognized in Islamic law and statutory laws that the penalties for different crimes overlap. Punishing the perpetrator of multiple crimes for a crime is considered an overlap in punishments. This research aims to apply the punishment on the criminal in the case of several crimes, as it shows the supremacy of Islamic law and its leniency towards its followers and criminals. The necessity of this research is that the intersection of punishments clarifies the aspects of pardon and facilitation in Islamic law for people who do not know the truth of Sharia law, as well as the provisions of Islamic jurisprudence in the Afghan penal code. The provisions are intended to clarify the agreement. In this research, an analytical and descriptive research strategy was chosen, and the writings of contemporary and previous jurists were studied. The jurists mentioned the intersection of punishments in their writings. The results of this study show that according to Islamic jurists and the provisions of the Afghan Penal Code, it is possible to determine punishment for several crimes, and several crimes are considered as one crime. The results of this research show that according to Islamic jurists and the provisions of the Afghan Penal Code, it is possible to determine punishment for several crimes, and several crimes are considered as one crime. If the mentioned cases differ, the penalties will not overlap.
The Nature of Guarantee Contract in Imamia Jurisprudence and Afghanistan Civil law
Authors:   دانش‌گاه/ پوهنتون تهران, امینی محمدعلی
Published: June 2024

There are two distinct views on the nature of the guarantee contract. In the famous view of Shia jurists, guarantee is transferable in nature, and it is beneficial to transfer liability to liability and acquit the liability of the main debtor. In this view, with the contract of guarantee, the debt is transferred to the liability of the guarantor and the liability of the main debtor is absolved. In another point of view, including in Afghanistan civil law, the guarantee has a concrete and collateral nature and is a contract according to which a third party undertakes to take care of the debtor if he does not pay his debt. Examining the reasons and views of the parties shows that the nature of the guarantee is other than transfer and attachment, and that payment and commitment to pay the debt and employment of the guarantor is due to it, without affecting the transfer or attachment of the guarantee. In principle, the addition of liability or its transfer are not required by the essence and nature of the gaurantee, although they may be required by its application.
Sociological Analysis of Alternative Sanctions to Imprisonment in Afghanistan Criminal Justice System
Authors:   دانش‌گاه/ پوهنتون بین‌المللی اهل‌بیت (ع), شریفی زیرکسار حسین
Published: June 2024

Imprisonment is one of the criminal reactions, which is applied to establishment of social order and ensure justice. Alternative punishments of imprisonment were proposed as a result of the emergence of critical criminology theories in the 19th century and de-imprisonment theories. According to the theories of the abolitionist schools, the movements of de-imprisonment and international documents, the Afghan legislator has determined the alternative punishments of imprisonment as the main punishment in 2016. The novelty of the alternative punishment of imprisonment as the main punishment in the penal code and how it matches with the prevailing culture needs a research in this regard. The purpose of the research is to study the alternative punishments of imprisonment, its function in reforming the criminal and how it is accepted as a social phenomenon in the Afghan society. The question is, what is the function of alternative prison sentences from a sociological point of view? What is the feedback of alternative prison sentences in correction and rehabilitation of criminals? This research has been done in a descriptive-analytical way, using a library tool to collect data, in the criminal law of Afghanistan. The findings of the research are; The legislator has determined the alternative punishments of imprisonment for the first time in its modern form as the main punishment. As far as the alternative punishments of imprisonment have been implemented in the society, according to the punishment of imprisonment, it has played more effective roles in the part of reformingthe criminal. Chnging imprisonment to an alternative punishment during punishment shows the flexibility of the legislator regarding the criminals in order to provide the basis for their reformation. However, lack of strict monitoring of its implementation, lack of necessary tools and equipment for electronic monitoring, and lack of culturalizaion among judges to determine alternative punishments to imprisonment are among the problems in its implementation.
Critical Analysis of the First Part of Mowlana's Biography
Authors:   دانش‌گاه/ پوهنتون فردوسی مشهد و دانش‌گاه/ پوهنتون هرات, عثمانی مژگان
Published: June 2024

The biographical account of Mowlana Jalaluddin Muhammad Balkhi, relying on ancient biographical sources and the general principle that the earliest manuscripts are the most authentic, has discredited many biographers who have not taken a critical and scientific approach to it. This research, based on credible sources, adopts a critical perspective on Mowlana's life, events, and experiences. The present study examines the first half of Mowlana’s life, which is divided into two parts: the first part covers Mowlana's birth until his education in jurisprudence and his role in delivering sermons and teaching sessions; the second part focuses on the development of ascetic Sufism with the guidance of Sayed Burhan al-Din Muhaqqiq Tirmidhi, which can be seen as a precursor to Mowlana’s later period of passionate mysticism. This research addresses the question: Was Mowlana the mystic, solely product of Shams Tabrizi's mystical teachings, or did the companionship of mentors like Sayed Burhan al-Din Muhaqqiq Tirmidhi, along with studying under teachers like Ibn al-Adim and others, as well as the influence of sociological factors such as political, social, geographical, and historical conditions, play an undeniable role in Mowlana's becoming a mystic? The findings indicate that despite the views of some Turkish and Western Rumi scholars, Mowlana’s birthplace was the city of Balkh, not Vakhsh. Additionally, Mowlana's process of spiritual asceticism and mystical path began before his meeting with Shams, guided by Burhan al-Din Muhaqqiq Tirmidhi.
Village Budgeting, Competence, and Their Impact on Organizational Performance and Community Satisfaction
Authors: Amelia Risa , Maulida Fina , Sutirman Wahdiat Irwan
Published: June 2024

Village governments play an important role in enhancing national development through effective budget management. Proper management of village funds can help improve organizational performance and community satisfaction. This study aims to identify the factors influencing village accountability in managing the Village Fund Budget and the competencies of Village Apparatus related to organizational performance. The research method used is qualitatif. The sampling technique used is purposive sampling, with respondents consisting of officials and the community in Beber Village. Data were obtained through a survey of 48 respondents. The results show that the accounting system affects the accountability of Village Fund Budget management, while the competence of village apparatus does not significantly affect the accountability of Village Fund Allocation management. This study implies that improving the village accounting system can enhance the accountability of village fund management, even though increasing the competence of village apparatus does not directly enhance such accountability. This indicates the need to focus on improving village accounting systems and procedures to achieve more accountable budget management.
The Turn as a Meta-ethical Event – Thinking Through and Beyond Heidegger’s Hints on the Problem of Evil
Authors: Kuravsky Erik ,   The Heidegger Circle
Published: June 2024

In Country Path Conversations, Heidegger relates both subjectivity and evil to rebellion or insurgency (Aufruhr, Aufstand). In other places (e.g., “Why Poets?”), Heidegger insinuates that the relation between subjectivity and a rebellion against the open is not accidental. In this essay, I lean on the Schellingian roots of the relations among subjectivity, evil, and rebellion as these appear in Heidegger’s 1936 lecture course and offer a further Beyng-historical interpretation of the source and the essence of evil. In this light, I view the un-grounded usurpation of metaphysical will to power in terms of a rebellion that seeks to posit the subject as the universal ground of beings and interpret the turn of the Event as a meta-ethical transformation of humanity in which the rebellious subjectivity is being overcome.
On the Right to Kick a Rock: The Argumentum ad Lapidem and Heidegger’s “The Argument against Need”
Authors: Polt Richard ,   The Heidegger Circle
Published: June 2024

“The argument against need” that is countered by Heidegger in his text by that name is likened to Samuel Johnson’s effort to “refute” Berkeleyan idealism by kicking a stone. Heidegger’s position is compared to ideas in Plato, Kant, and Scheler. The Heideggerian correlation or “need” between being and the human cannot be refuted by facts about entities such as stones, but the argumentum ad lapidem does retain a certain right as an experience of resistance.
Being before Time? Heidegger on Original Time, Ontological Independence, and Beingless Entities
Authors: Keiling Tobias ,   The Heidegger Circle
Published: June 2024

In the recently published manuscript “The Argument against Need” (ca. 1963), Heidegger discusses the notion of being-in-itself (Ansichsein) with regard to entities that predate the existence of knowers. Section 1 introduces the problem of so-called “ancestral facts,” which Meillassoux and Boghossian have used to argue for a specific form of realism. Sections 2 identifies a specific understanding of time as the basis for their argument. Sections 3–4 show how Heidegger rejects this account of time. Section 5 describes the general form of ontologies that deny entity independence (dependence ontology). Section 6 turns to Heidegger’s account of a resistance to ontological sense-making in what he calls “beingless” (seinlos) entities. Discussing work by Haugeland and Wrathall, I conclude in section 7 that Heidegger’s response to the “argument against need” is to reject the idea of unidirectional dependence in favor of a triadic interdependence between being, entities, and us.