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Volume 1 - Issue 2 (2023)

All Articles
Original Article27 downloads
Analyzing the dynamic data of Mashhad metro line 1 tunnel using seismic table
DOI: https://doi.org/10.5281/zenodo.13948872
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Metro is a type of public transportation system with high capacity, which is mostly established in urban areas. Unlike a bus or a tram, the subway is a type of electric rail system that works on its special tracks, and pedestrians or any other vehicles do not have access to this track, and it mostly moves in tunnels or elevated non-level rails. Modern metro services are usually provided by electric self-propelled trains that run on railway tracks, although there are also some trains with rubber wheels or magnetic levitation. Metro stations are usually higher than the train tracks and do not have any stairs to the train. These platforms usually have a space compared to the train, so to reduce this space between the platform and the train, custom trains need to be built. As the second largest religious city in the world, Mashhad welcomes more than 22 million travelers every year. The comprehensive transport studies of Mashhad required 4 metro lines. Line 1 has been constructed and operated using the slow method, and Line 2 is in the operation stage. Line 2 of the Mashhad metro runs north-south and is 14 kilometers long, with a diameter of 9.43 meters, and passes through important parts of the city. This research, using a quantitative methodology and using a model tunnel, and a seismograph, has recorded the incoming earthquakes and predicted the resilience of this subway line in the city of Mashhad in 2024. For all the tunnel tests, the bending moment and deformations of the tunnel increase with the increase of the maximum base acceleration, but the location of the minimum and maximum values of the moment remain constant. The largest bending moment values appear in the middle of the crown and shoulder of circular tunnels. For the acceleration of A=0.27g, with the increase of the applied frequency to the system, the bending moment is constant or slightly decreases, but for A=0.43g, the maximum moment decreases strongly with the increase of the frequency.
Original Article40 downloads
AI/ML-Powered Phishing Detection: Building an Impenetrable Email Security System
DOI: https://doi.org/10.5281/zenodo.13948722
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The most extensive threat type, for the present moment at least, remains phishing attacks that rely on people’s susceptibility to trick them into sharing personal data. Such attacks generally consist of fake emails originating from an apparently reliable source, for instance, businesses, banks or government facilities. While normal forms of email filtering prove somewhat useful in the fight against phishing, the techniques are highly unlikely to catch the modern-day complex phishing channels such as zero-day phishing or spear phishing. As a result of this, there is a growing uptake of applying Artificial Intelligence (AI) and Machine Learning (ML) to address email security. They are capable of training on sampled volumes of emails and then using this training to improve the recognition of phishing and non-phishing instances. Here, we propose to work on a system of phishing detection using AI/ ML, which will be instrumentally crucial in making email security reliable and adaptable. The system employs Random Forest, Support Vector Machines (SVM) as well as Neural Networks to classify the emails based on the features extracted from the subject, body as well as links of the emails. By employing both phishing and innocuous email corpus, we trained and tested these models for the purpose of understanding the viability of phishing identification. The achievements of the study were the improved accuracy of detection when compared to conventional approaches and the further reduction of misrecognition, which improves security in general. It should be noted that by integrating a multi-model approach with learning mechanisms, the proposed system is indeed versatile and strong against advanced phishing threats.
Original Article436 downloads
Data Visualization for Business Analysts: Converting Numbers into Narratives
DOI: https://doi.org/10.5281/zenodo.14993959
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Data visualization is a critical tool for business analysts, enabling the transformation of raw data into meaningful insights that drive decision-making. This paper explores the theoretical and practical foundations of data visualization, emphasizing its role in business analytics. We discuss key principles, cognitive theories, and visualization techniques, alongside modern tools and technologies. Additionally, we highlight the importance of data storytelling, best practices, and evaluation methods to assess the impact of visualizations. Emerging trends such as AI-driven visualization and immersive analytics are also explored, providing a comprehensive outlook on the future of data visualization for business analysts.
Original Article41 downloads
Artificial Intelligence in the Conservation of Iranian Architectural Heritage: Analytical Reconstruction and Color Restoration of Sheikh Lotfollah Mosque
DOI: https://doi.org/10.5281/zenodo.17616954
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This research investigates the application of artificial intelligence (AI) to the study and conservation of the Sheikh Lotfollah Mosque, one of the most celebrated monuments of Safavid architecture in Isfahan, Iran. Using a multi-layered methodology, the study evaluated the performance of AI systems in five key domains: crack detection, geometric motif reconstruction, generative modeling, color restoration, and image enhancement. Results demonstrated that convolutional neural networks achieved recall rates of 96% in identifying micro-cracks, thereby outperforming traditional manual inspection. In motif reconstruction, polygons were restored with structural similarity (SSIM) values above 0.89, while arabesques and calligraphy remained more challenging. Generative adversarial networks produced geometrically sharp motifs, whereas diffusion models excelled in perceptual realism, suggesting hybrid potential for conservation practice. In color restoration, ΔE values remained below or near the perceptual threshold of 3.0, confirming chromatic authenticity, while image enhancement improved PSNR by 14.2 dB, revealing micro-cracks and glaze details previously undetectable. Symmetry and tessellation analysis validated that AI internalized the mathematical rules of Safavid ornament, although its tendency toward perfection underscored the importance of human oversight in preserving authenticity. The findings establish AI as a reliable diagnostic and reconstructive partner while also raising questions of authenticity, authorship, and cultural stewardship. The study concludes that AI should be framed as a digital apprentice in heritage science, capable of extending the life of Iranian architectural traditions in both material and computational domains.
Original Article37 downloads
AI-Powered Financial Risk Modeling in Tokenized Asset Environments: A Network-Based Analysis
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This study examines the intersection of artificial intelligence and decentralized finance by proposing a network-based approach to modeling systemic financial risk in tokenized asset ecosystems. The conceptual foundation redefines risk as a relational property of graph structures rather than a scalar function, emphasizing the emergent behavior of decentralized financial protocols. The methodology integrates directional graph attention networks, specifically the DEDGAT architecture, trained on real and synthetic blockchain data to assess both inbound vulnerability and outbound contagion. Empirical findings demonstrate that the model significantly outperforms traditional benchmarks in identifying early-warning indicators, accurately detecting structurally critical nodes, and providing interpretable risk signals. The results confirm that directed graph embeddings enable more granular and adaptive risk stratification than symmetric or tabular models. The discussion of the findings situates this work within the latest literature on graph neural networks in finance, highlighting its contributions to regulatory visibility, DAO governance, and real-time monitoring. The conclusion affirms the effectiveness of DEDGAT as a scalable and policy-relevant tool for navigating the systemic fragilities of tokenized financial systems, while acknowledging the limitations imposed by synthetic validation data and current interpretability constraints.
Original Article4 downloads
Comparative Assessment of CFRP and Steel Jacketing Techniques for Post-Fire Strengthening of Reinforced Concrete Structures
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Fire exposure can cause severe degradation in the mechanical and deformation characteristics of reinforced concrete structures, necessitating effective post-fire strengthening strategies. This study presents a controlled experimental investigation comparing the performance of carbon fiber reinforced polymer (CFRP) confinement and steel jacketing techniques for post-fire strengthening of reinforced concrete columns. Identical column specimens were subjected to controlled fire exposure, followed by strengthening using either CFRP wraps or steel jackets, and subsequently tested under axial compression. The experimental program evaluated residual load-bearing capacity, stiffness recovery, deformation capacity, and energy absorption. The results demonstrate that both strengthening techniques significantly improve post-fire performance, albeit through distinct mechanical mechanisms. CFRP confinement primarily enhances deformation capacity and post-peak stability, while steel jacketing provides superior strength and stiffness recovery. Three-dimensional performance domains reveal clear trade-offs between strength, stiffness, and ductility, highlighting that no single technique is universally optimal. The findings emphasize the importance of performance-based selection of post-fire strengthening strategies based on fire severity and structural performance objectives.

Original Article18 downloads
Developing Material Passports for Circular Concrete Management: Integrating Life Cycle Assessment and Blockchain
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The transition of the construction sector from a linear take-make-dispose model to a circular economy is critically dependent on the availability of reliable, comprehensive information about the materials embedded in the built environment. Concrete, as the world's most consumed construction material, presents a particularly acute challenge: its production accounts for a significant share of global carbon emissions, yet the vast quantities of concrete currently in service remain undocumented and therefore invisible to circular recovery markets. Material passports have been proposed as a digital instrument to address this informational void, but existing passport concepts remain static, lack integration with environmental assessment methodologies, and fail to provide the trust guarantees necessary for transactions among independent actors. This study develops and empirically evaluates an integrated material passport framework for circular concrete management that combines a structured, standards-aligned data model with a dynamic lifecycle assessment engine and a permissioned blockchain trust layer. The research applies a design science methodology, progressing through the formalisation of a 58-field passport schema, the construction of a modular lifecycle assessment computational engine that recalculates environmental impacts as lifecycle events are recorded, the configuration and testing of a blockchain architecture employing Raft consensus and off-chain storage, and the application of the integrated framework to four illustrative circularity scenarios derived from real-world European concrete supply chain data. The passport data model was derived through a criterion-based filtering of 137 candidate fields, achieving a Fleiss’ kappa of 0.84 among domain experts and 72 per cent alignment with existing ISO and CEN standards. The dynamic lifecycle assessment engine demonstrated that tracking a concrete element through multiple use cycles reveals a global warming potential of 171.5 kg CO₂-eq per cubic metre under a three-cycle reuse and recycling scenario, compared with the 327.4 kg CO₂-eq that a static environmental product declaration would report for a single linear cycle, capturing a net environmental benefit of up to 265.9 kg CO₂-eq per cubic metre for intact structural reuse. The permissioned blockchain testnet, configured with four validator nodes, achieved a tamper detection rate of 1.00 across ten deliberate off-chain data alteration attempts with a mean verification latency of 2.7 seconds and an annual network energy consumption of 2,803 kWh, representing less than 0.01 per cent of the global warming potential savings enabled by the system. The Monte Carlo-based economic analysis identified a positive net present value of €2,870 to €3,960 per cubic metre for intact reuse scenarios, with viability thresholds of a virgin concrete price above €120 per cubic metre and a discount rate below 5.8 per cent, and demonstrated that a €40 per tonne carbon price extends economic viability to lower-grade recycling pathways. The study confirms that the integration of material passport design, dynamic lifecycle assessment, and blockchain technology into a single operational framework is technically feasible, environmentally meaningful, and conditionally economically viable. The findings provide an evidence-based template for industry practitioners, standardisation bodies, and policymakers seeking to operationalise digital product passports for construction materials and to advance the circular transition of the concrete sector.