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5th International Conference on Data Science, Machine Learning and Applications, ICDSMLA 2023

作者： Ansari, Tabish Sekhar, Malepati Chandra Alam, Intekhab Bargavi, Manju Department of Civil Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bengaluru India

ISBN: (纸本)9789819780426

Material welding is used in expanding industrial and industrial applications and depends on accuracy and precision to ensure satisfaction and protection. Laser inspection technology has been developed to help enhance the procedure's accuracy and performance by presenting rapid, reliable imaging that may be processed to decide where changes are wished. Through photograph processing, particular modifications to cloth welding can be recognized quickly and effectively. Additionally, photograph-processed laser inspection can hit upon any irregularities or defects in the welding method that can cause premature failure or decreased functionality. With this generation, designers and manufacturers can also develop higher control structures for his or her tasks, making more accurate and price-effective use of their assets. Sooner or later, the laser inspection era can offer actual-time remarks for automatic tactics, allowing changes to be made quickly with minimal disruption. Using this image processing technology, fabric welding groups can improve exceptional accuracy and value across merchandise. Optimizing material welding through image-processed laser inspection is a valuable method for controlling the pleasantness of welds. This technique uses a laser scanner, mild source, and a digital camera to seize detailed photos of the welds before and after welding. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Inspection

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Application of Machine Learning Predicting Injuries in Traffic Accidents through the Application of Random Forest

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Recent Patents on engineering 2025年第2期19卷 108-121页

作者： Singh, Veer Bhadra Pratap Hemamalini, V. Muttipati, Appala Srinuvasu Raju, S. S. S. V. Gopala Shatil, Abu Hena Md Sharma, Abhishek Department of Cyber Security School of CSIT Symbiosis Skills and Professional University Kiwale Pune India Department of Networking and Communications School of Computing SRM Institute of Science and Technology Chennai India Bangalore India Department of Civil Engineering Rajiv Gandhi University of Knowledge Technologies Nuzvid Campus Andhra Pradesh 521202 India Dhaka Bangladesh Shri Vaishnav Vidyapeeth Vishwavidyalaya Madhya Pradesh Indore India

Background: The objective of this work is to analyze and predict the harmfulness in traffic accidents. Method: Several Random Forest statistical models are created, in which the predictable variable (response/ output variable) is the harmfulness of the accident, while the input variables are the various characteristics of the accident. In addition, these generated models will allow estimating the influence or importance of each of the factors studied (input variables) concerning the harmfulness of road accidents so that it is possible to know in which aspects it is more profitable to work with the objective of reducing mortality from traffic accidents [1]. Results: In this regard, the predictive algorithm has an out-of-bag error of 26.55% and an overall accuracy of 74.1%. Meanwhile, the local accuracy of the mildly wounded class is 66.1% compared to 81.4% of the dead and severely wounded class, which, as mentioned, has higher prediction reliability. Conclusion: Finally, it is worth noting the enormous usefulness of the Random Forest machine learning technique, which provides very useful information for possible research or studies that may be carried out. In the specific case of this patent work, through the use of the R programming language, which in turn presents a wide range of freely accessible utilities and functions with which it may be interesting working, it has generated results of great value for this area of activity, important to society as road safety. © 2025 Bentham Science Publishers.

关键词： Highway accidents

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IoT-Enabled Smart Irrigation System with ABiLSTM for Optimized Water Management and Crop Yields 3

IoT-Enabled Smart Irrigation System with ABiLSTM for Optimiz...

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3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Chaturvedi, Abhay Rao, Praveena Venkata Maruti Prasad, S. Malini, T. Prabagar, S. Bharathi, K. Gla University Department of Electronics and Communication Engineering Mathura India Institute of Aeronautical Engineering Department of Civil Engineering Hyderabad India Aditya Institute of Technology & Management Department of Bs & H Tekkali India Sri Krishna College of Engineering and Technology Department of Electrical and Electronics Engineering Coimbatore India School of Advanced Computing Alliance University Department of Computer Science and Engineering Bangalore India Erode Sengunthar Engineering College Department of Agricultural Engineering Perundurai India

ISBN: (纸本)9798331508456

An efficient management of water resources in agriculture is now crucial in light of the growing global population and increased demand for water. Innovative solutions are needed to improve water efficiency since conventional irrigation methods are not sustainable anymore. Through the use of precision agriculture and the Internet of Things (IoT), smart irrigation has revolutionised water management, leading to increased crop yields. In order to improve the efficiency of decision-making, this research presents an Internet of Things (IoT) smart irrigation system that uses SA-BiLSTM (Self-Attention Bidirectional Long Short-Term Memory). Using Min-Max and Z-score normalisation approaches for feature scaling, the methodology includes data preparation, feature selection, and model training. Methods based on ranking, such as the Chi-Square Test, Pearson Correlation Coefficient (PCC), and Mutual Information (MI), were used to prioritise feature selection. Classification and prediction testing showed that the proposed SA-BiLSTM model outperformed state-of-the-art models like HGAT and Linear SVM, with an astounding 95.63% accuracy. Improved resource allocation and irrigation effectiveness, leading to increased agricultural output, are highlighted by the results, which highlight the effectiveness of advanced machine learning approaches in precision agriculture. © 2025 IEEE.

关键词： Resource allocation

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Predictive Intelligence Enhanced Fuzzy Model for Underwater Network Optimization 5th

Predictive Intelligence Enhanced Fuzzy Model for Underwater ...

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5th International Conference on Data Science, Machine Learning and Applications, ICDSMLA 2023

作者： Praveena, K.N. Mishra, Awakash Kamalraj, R. Belwal, S. Ankit School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Department of Computer Science and Information Technology Jain University Karnataka Bangalore India Department of Civil Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Manduwala Dehradun Navgaon India

ISBN: (纸本)9789819780426

This paper gives a predictive intelligence-enhanced fuzzy version for underwater network optimization. It combines predictive intelligence technology with fuzzy common sense, which can lessen the functionality gap of underwater community optimization. This proposed version is implemented in an underwater cooperative tracking community. With the proposed model, objectives can be tracked with sensors within the network, and their national statistics can also be appropriately anticipated. Similarly, the version has excessive scalability and robustness because of the mixing of predictive intelligence. In the end, simulation experiments suggest that the proposed model correctly improves the precision of tracking challenges and improves the scalability of the network. The Predictive Intelligence superior Fuzzy model (PIE-FUZMO) for underwater network optimization is a singular method developed to cope with the complex optimization issues confronted through underwater networks. The PIE-FUZMO uses a couple of laptops with imaginative and prescient sample recognition techniques and fuzzy good judgment to enhance the underwater community performance while decreasing the running fee. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Scalability

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Field data driven rockfall hazard and risk assessment along Sangla-Chitkul road, Himachal Pradesh, India

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Bulletin of engineering Geology and the Environment 2025年第6期84卷 1-19页

作者： Pandey, Vishnu Himanshu Ratnam Kushwaha, Gaurav Kainthola, Ashutosh Yadav, Vikas Singh, TN Krishna, Abhi S Geoengineering and Computing Laboratory Department of Geology Banaras Hindu University Varanasi India Department of Civil & Environmental Engineering IIT Patna Patna Bihar India

This study addresses the issue of rockfall hazard in Baspa Valley, Himachal Pradesh, India. Proven empirical rockfall hazard rating systems were interpolated in the GIS-environment to analyse the contingent risk to the population. Initially, field data were ascertained from precarious rockfall locations along the 23 km long Sangla-Chitkul road. This was followed by a kinematic analysis to identify potential structural failure modes, revealing that each studied slope section could undergo one or a combination of failures. Rockfall Hazard Rating System (RHRS) parameters were formulated and interpolated for the entire area using the inverse-distance weighting (IDW) technique. Resulting hazard map, overlaid with population data, classified rockfall risk into five categories: very high (14.7%), high (28.8%), moderate (23.2%), low (18.6%), and very low (14.7%). A similar assessment using the Missouri Rockfall Hazard Rating System (MORFH-RS) showed the following rockfall risk distribution: very high (11%), high (21%), moderate (21%), low (21%), and very low (11%). Additionally, MORFH-RS indicated that around 90% of the area lies in high-risk and high-consequence zone, with high consequences for all locations. Two-dimensional stochastic simulations were conducted to understand rockfall dynamics at all studied locations, revealing that most sites exhibit kinetic energy exceeding 500 kJ, with five locations surpassing 1000 kJ. This indicates a high potential for significant damage across a large area of the valley, based on runout-distance data. Additionally, these findings were correlated with geotechnical characterization using Global Slope Performance Index (GSPI), identifying the potential for four distinct failure types in the valley.

关键词： Rockfall Kinematic analysis Hazard and risk assessment RHRS MORFH-RS GSPI

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NATO AVT-350 Task Group: Innovative Control Effectors for Manoeuvring of Air Vehicles - Advanced Concepts - Introduction and Overview

NATO AVT-350 Task Group: Innovative Control Effectors for Ma...

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AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Forster, Matt Seidel, Jürgen Warsop, Clyde BAE Systens AIR Filton Bristol United Kingdom High Performance Computing Research Center Department of Aeronautics US Air Force Academy United States School of Civil Aerospace and Design Engineering University of Bristol United Kingdom

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

Active flow control (AFC) holds the potential to transform the flight control of current and next generation aircraft by eliminating conventional, mechanically-deflected control surfaces. Such a system could reduce the weight and complexity of the vehicle while maintaining the same or superior system performance. A recent NATO research task group has investigated a suite of AFC-based flight control systems for use in next-generation tailless military aircraft. The group has evaluated and generated performance data for individual AFC technologies on representative aircraft and developed flight control models from these data. Simulated mission scenarios were then ‘flown’ with the models to inform a systems/trades assessment of the AFC flight controlled vehicle. This paper describes the motivation for the study, the formation and organization of teams within the task group, and an introduction to the specific tasks undertaken by each team. Companion papers describe in greater detail each part of the task group’s effort. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Fighter aircraft

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Deep Learning and Explainable AI Based Blast Wave Pressure Prediction in IoV Applications

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Procedia Computer Science 2025年 252卷 270-278页

作者： Mahmood Hussain Mir Judeson Antony Sulaiman Syed Mohamed Soumi Dhar Pragya Danish Fayaz Alliance School of Advanced Computing Alliance University Bangalore India Department of Civil Engineering Alliance College of Design Alliance University Bangalore India

Predicting blast wave pressure is crucial for enhancing safety and response strategies in Internet of Vehicles (IoV) applications, particularly in the context of urban environments and high-risk areas. This paper presents a novel deep learning model designed for predicting blast frequencies in BLEVE (Boiling Liquid Expanding Vapor Explosion) scenarios. The article evaluates several activation functions, including ReLU, Mish, ELU, Silu, and Leaky ReLU, to determine their effectiveness in improving model accuracy. The results indicate that the Mish activation function achieves slightly lower Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) scores on the test dataset, with Train RMSE at 0.075 and Test RMSE at 0.099. The Silu activation function also demonstrates strong performance, yielding a Train RMSE of 0.074 and Test RMSE of 0.095, alongside high R 2 scores, indicating a good fit to the data. The paper further explores the interpretability of the model’s predictions using the LIME framework, revealing insights into how sensor positions and vapor heights influence blast frequency predictions. This research explores the potential of deep learning techniques in enhancing safety measures and predictive capabilities in hazardous scenarios, contributing valuable insights to the field of risk assessment and management in IoV applications.

关键词： Blast Wave Pressure Deep Leaning Explainable Artificial Intelligence Prediction

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Intelligent Gradient Boosting Algorithms for Estimating Strength of Modified Subgrade Soil

arXiv

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arXiv 2025年

作者： Mustapha, Ismail B. Abdulkareem, Muyideen Hasan, Shafaatunnur Ganiyu, Abideen Nabus, Hatem Lee, Jin Chai Department of Computer Science School of Computing Universiti Teknologi Malaysia Malaysia Department of Civil Engineering Faculty of Engineering and Built Environment UCSI University Kuala Lumpur Malaysia Department of Civil Engineering British University of Bahrain Saar Bahrain

The performance of pavement under loading depends on the strength of the subgrade. However, experimental estimation of properties of pavement strengths such as California bearing ratio (CBR), unconfined compressive strength (UCS) and resistance value (R) are often tedious, time-consuming and costly, thereby inspiring a growing interest in machine learning based tools which are simple, cheap and fast alternatives. Thus, the potential application of two boosting techniques;categorical boosting (CatBoost) and extreme gradient boosting (XGBoost) and support vector regression (SVR), is similarly explored in this study for estimation of properties of subgrade soil modified with hydrated lime activated rice husk ash (HARSH). Using 121 experimental data samples of varying proportions of HARSH, plastic limit, liquid limit, plasticity index, clay activity, optimum moisture content, and maximum dry density as input for CBR, UCS and R estimation, four evaluation metrics namely coefficient of determination (R2), root mean squared error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) are used to evaluate the models’ performance. The results indicate that XGBoost outperformed CatBoost and SVR in estimating these properties, yielding R2 of 0.9994, 0.9995 and 0.9999 in estimating the CBR, UCS and R respectively. Also, SVR outperformed CatBoost in estimating the CBR and R with R2 of 0.9997 respectively. On the other hand, CatBoost outperformed SVR in estimating the UCS with R2 of 0.9994. Feature sensitivity analysis shows that the three machine learning techniques are unanimous that increasing HARSH proportion lead to values of the estimated properties respectively. A comparison with previous results also shows superiority of XGBoost in estimating subgrade properties. © 2025, CC BY.

关键词： Sensitivity analysis

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Al-anodizing waste as a supplementary cementitious material for 3D-printed Portland cement

Waste Management Bulletin

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Waste Management Bulletin 2025年第2期3卷 10-20页

作者： da Silveira Júnior, Jairon Gomes Dors Sakata, Rafael Onghero, Lucas de Matos, Paulo Ricardo Rodríguez, Erich David Simão, Lisandro Novaes de Oliveira, Antonio Pedro de Araújo Moura, Ruan Carlos Maduro de Campos, Carlos Eduardo de Castro Pessôa, José Renato Tramontin Souza, Marcelo Department of Engineering and Computing State University of Santa Cruz (UESC) BA Ilhéus 45662-900 Brazil Academic Department of Civil Construction Federal Technological University of Paraná (UTFPR) PR Curitiba 80230-901 Brazil Department of Civil Engineering Federal University of Santa Catarina (UFSC) SC Florianópolis 88037-000 Brazil Department of Civil Engineering Center for Technological Sciences Santa Catarina State University (UDESC) SC Joinville 89219-710 Brazil Department of Structures and Civil Construction Technology Center Federal University of Santa Maria (UFSM) RS Santa Maria 97105-900 Brazil Research Group on Sustainability and Waste Management University of Ribeirão Preto (SP) 14096900 Brazil Department of Mechanical Engineering Federal University of Santa Catarina (UFSC) SC Florianópolis 88040-900 Brazil Department of Physics Federal University of Santa Catarina (UFSC) SC Florianópolis 88040-900 Brazil

This study investigates the use of Al-anodizing waste (AAW), a byproduct of the anodizing process for aluminum and its alloys, in cement composites designed for 3D printing applications. For cementitious materials to be suitable for 3D printing, they must maintain adequate stability to support overlapping layers while also exhibiting sufficient flowability and open time for effective pumping. The incorporation of AAW aims to improve the rheological properties of these mixtures, enhancing both their open time and buildability. To evaluate the potential of AAW in 3D printing cement composites, a comprehensive approach was adopted. This included rheological analysis, flow table testing, investigations into buildability, and assessments of mechanical performance. Additional techniques, such as isothermal calorimetry and in situ X-ray diffraction, were employed to examine the effect of AAW on the hydration kinetics of cement pastes. The findings highlighted several advantages of incorporating AAW into cement-based formulations, such as improved buildability, quick adjustments in rheological properties, prolonged open times, enhanced mechanical performance, cost-effectiveness, and increased sustainability by transforming a commonly discarded material into a valuable resource for civil construction. © 2025 The Authors

关键词： 3D printing Additive manufacturing Al-rich waste Buildability Open time

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Towards sustainable sand resource management - Best governance practices and alternatives to river sand in India

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Environmental Science and Pollution Research 2025年 1-28页

作者： Bhatawdekar, Ramesh Murlidhar Mohamad, Edy Tonnizam Singh, Trilok Nath Chengong, Dato Hock Soon Kainthola, Ashutosh Khandelwal, Manoj Chihi, Feten Rathinasamy, Vynotdni Arthur, Clement Kweku Deshpande, Anand Ravi Nigam, Sanjay Kwon, Sangki Ralegaonkar, Rahul Azad, Md Alquamar Universiti Teknologi Malaysia Johor Bahru Skudai Malaysia Deparment of Mining Engineering Indian Institute of Technology Kharagpur India Indian Institute of Technology Bihar Patna India Malaysia Quarry Association Selangor Kuala Lumpur Malaysia Geoengineering & Computing Laboratory Department of Geology Banaras Hindu University Varanasi India Institute of Innovation Science and Sustainability Federation University Australia BallaratVIC3350 Australia Faculty of Civil Engineering Budapest University of Technology and Economics Budapest Hungary School of Civil Engineering Universiti Sains Malaysia Penang Malaysia Department of Mining Engineering Faculty of Mining and Minerals Technology University of Mines and Technology Western Region Tarkwa Ghana SURU09 Business Services Thane India Department of Energy Resources Engineering Inha University Incheon Korea Republic of Department of Civil Engineering Visvesvaraya National Institute of Technology Nagpur India

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