Engineering Commons^™

Forecast Of Mining Fatalities Using Machine Learning Algorithms, Enoch Nii-Okai

Journal of Sustainable Mining

The mining industry plays a crucial role in economic development, but is often accompanied by notable occupational hazards, including fatalities. This research investigates the application of machine learning techniques to predict mining fatalities in the United States using historical data from 1983 to 2023, sourced from the National Institute for Occupational Safety and Health (NIOSH) and FRED Economic Data. Predictive models, including random forest (RF), gradient boosting (GB), decision tree (DT), multiple linear regression (MLR), and linear regression (LR), were employed to assess relationships between operational parameters and annual fatality counts. The dataset was thoroughly preprocessed, addressing missing values and …

Effect Of Cryogenic Soaking Duration On The Crystallographic Structure And Corrosion Resistance Of Astm A36 Steel, Sukri Mubarok, Andoko Andoko, Retno Wulandari

Journal of Mechanical Engineering Science and Technology (JMEST)

This study investigates the influence of cryogenic soaking duration on the crystallographic characteristics and corrosion resistance of ASTM A36 structural steel. Specimens were immersed in liquid nitrogen at −196°C for 0, 12, 36, and 60 hours. Crystallographic analysis was conducted using X-ray diffraction (XRD), while electrochemical corrosion behavior was assessed through Tafel polarization in 3.5% NaCl solution using an Autolab PGstat. Key parameters evaluated included crystallite size, microstrain, dislocation density, corrosion rate, and material degradation. The results showed that soaking up to 36 hours significantly refined the microstructure, as indicated by reduced crystallite size and increased dislocation density and microstrain. …

Thermal Performance Analysis And Heat Transfer Characteristics Of A Solar Powered Paddy Dryer Using A Flat Plate Solar Collector, Peter Sahupala, Daniel Parenden

Journal of Mechanical Engineering Science and Technology (JMEST)

Freshly harvested paddy holds much water (23.00% wet basis in this study) and spoils quickly without prompt drying. Open sun drying depends on the weather and heats the grain unevenly. This study tested an indirect solar paddy dryer with a 0.60 m² flat plate collector, a 10 kg capacity, and natural convection airflow in Merauke, South Papua. Six thermocouples (collector, drying chamber, and chimney), a pyranometer, and a moisture meter recorded data every 15 minutes at drying temperatures of 40°C, 50°C, and 60°C. The data gave useful heat energy, collector thermal efficiency, and drying rate. Useful heat energy ranged from …

The Effects Of Transportation Distribution On Dry Ice Insulated Containers' Thermal Performance, Edmund Tang

Journal of Applied Packaging Research

This study builds upon methodologies established in a 2024 investigation of the combined effects of physical distribution stresses and thermal environments on cold chain packaging performance. Prior results showed that moisture-resistant gel packs generally maintained a vaccine storage range of +2°C to +8°C. However, when exposed to extreme combined stresses, thermal performance degradation of up to 13.5% was observed. The present work applies a similar experimental framework to transportation systems that rely on dry ice as the primary cooling medium, addressing the distinct challenges associated with ultra-cold distribution.

Pharmaceutical products requiring dry ice are highly temperature-sensitive and vulnerable to performance …

Quantifying Attacker Payoff In Harvest Now, Decrypt Later Attacks: A Persistence-Based Framework For Trade Secret Risk, Arjun Kumar, Gene Yang, Turiya Misra, Francis Kagai

Journal of Cybersecurity Education, Research and Practice

Harvest-now, decrypt-later (HNDL) assumes that encrypted secrets stolen today will remain worth decrypting when quantum computers eventually make that possible. But what if they do not? This paper models HNDL from the attacker’s perspective by asking a different question: how much will a stolen secret still be worth when it can finally be decrypted? We develop a conditional retained-value framework combining probabilistic Q-day timing, information depreciation, and adversary-access delay. The results reveal a sharp divide. Rapidly depreciating secrets can lose much of their strategic value before quantum decryption becomes useful, while persistent secrets can retain substantial attacker payoff even under …

Delivery Approach – Read Me First Guide, Robert Moore

Reports

This Build Digital guidance provides a structured approach to selecting and implementing information management delivery arrangements for public sector construction projects in Ireland, supporting the Capital Works Management Framework (CWMF), the Irish BIM Mandate and the ISO 19650 series.

The guidance considers four delivery approaches: In-house, Third-party, Design Team Lead and Sub-consultant. It supports public sector clients in evaluating their organisational capability, available resources, project complexity, governance requirements and associated risks to determine the most appropriate delivery approach.

The accompanying four-document suite comprises a Read Me First implementation guide, a comparative guide to the delivery approaches, a project-specific risk assessment …

Project Information Standard - Asset Register Building, Robert Moore

Tools

This Build Digital Asset Register for Buildings provides a practical starting point for public sector clients developing asset information requirements for conventional building projects.

Based on a simplified selection of COBie information fields, the Excel workbook supports the identification, organisation and collection of asset information for building operation and maintenance. It can assist clients in defining information requirements and communicating these to project delivery teams.

The workbook is particularly suited to organisations beginning their digital asset management journey. It is intended as an adaptable guidance resource rather than a comprehensive asset management solution. More complex buildings and critical infrastructure may …

Understanding Structured Data From The Perspective Of Irish Industry Professionals, Hamza Tariq, Ciaran Mcnally

Reports

This report examines the challenges associated with adopting structured data across the Irish construction sector, drawing on interviews with industry professionals involved in design and construction.

It explores why unstructured information remains prevalent and identifies barriers including digital knowledge gaps, inconsistent information requirements, fragmented information management systems, established working practices and contractual arrangements.

The report provides recommendations to support greater adoption of structured data, improved interoperability and more effective information exchange, helping organisations realise the benefits of digital construction.

Getting Started With Digitalisation: A Guide To Meeting Environmental Policy Requirements In The Built Environment, Conor Shaw, James .O'Donnell

Reports

This guide supports organisations across the built environment sector in adopting digital processes to meet environmental policy requirements and improve lifecycle decision-making.

It provides a practical five-step approach to digitalisation: establishing requirements, defining scope, collecting asset data, conducting calculations and interpreting results.

The guide explores how structured information and digital workflows can support environmental reporting, carbon accounting, lifecycle cost analysis and asset management, using practical examples to illustrate their application.

It helps organisations identify suitable starting points, assess their capabilities and make informed decisions about digital adoption.

Design Team Lead Delivery Approach - Implementation Guide, Robert Moore

Reports

This Build Digital guidance provides a practical framework for implementing the Design Team Lead information management delivery approach on public sector construction projects in Ireland, aligned with the Capital Works Management Framework (CWMF), the Irish BIM Mandate and the ISO 19650 series.

The approach enables the Design Team Lead to undertake additional information management services on behalf of the Client (Appointing Party), while the Client retains responsibility for governance, approval of information requirements and acceptance of information.

The four-document suite comprises an implementation guide, an activities schedule and two detailed scopes of services covering Standard and Complex project requirements. These …

1 / 2 Toward A Core Domain Ontology For Road Asset Management: A Consolidated Model Of Domain Requirements, Hamza Tariq, Ciaran Mcnally

Reports

This research explainer summarises a study examining how a shared conceptual model can improve interoperability in road asset management. Drawing on a systematic review of existing ontology research, the study consolidates key domain requirements into a Unified Modelling Language (UML) model. The findings provide a foundation for developing a core domain ontology to support more consistent interpretation, exchange and integration of road asset information across BIM, GIS and asset management systems.

Prototyping Pinhole Collimators For Imaging Irradiated Nuclear Fuel, Narrie Loftus, Ashish Avachat, Joseph T. Graham, Ryan Fronk, Jordan Fox, Rachel Shaffer, Seth Kilby

Nuclear Engineering and Radiation Science Faculty Research & Creative Works

Collimator design and fabrication is a costly and time-consuming process that does not lend itself to simplified testing. This manuscript presents an approach for the rapid prototyping of sub-millimeter pinhole collimators for nuclear fuel applications using low-melting-point, high-mass-density materials. Multiple collimators were fabricated to test collimator parameters and properties, including acceptance angle, aperture diameter, and magnification. The fabricated collimators were paired with a position-sensitive CdZnTe detector to determine the detector response convolved with the collimator. Preliminary analysis using these collimators showed that they were effective in collimating multi-energy gamma laboratory sources. Radiography was performed to confirm magnification and determine coarse …

A Review Of Gallium And Germanium Recovery From Industrial Solid Residues, Ernest V. Oteng, Marthias Silwamba, Lana Alagha, Alex Luyima

Mining Engineering Faculty Research & Creative Works

The global demand for critical elements such as gallium (Ga) and germanium (Ge), and the shift toward circular mining has accelerated the development of new extraction and recovery processing these metals from industrial solid residues. This transition is driven by a substantial surge in demand for advanced technologies and the depletion of primary ore. Consequently, industrial solid residues, particularly zinc processing residues, smelter slags, flue dusts, and coal fly ash, are emerging as viable alternative sources. These residues incorporate Ga and Ge through isomorphic substitution in phases such as ferrites, silicates, and glassy aluminosilicates, and are enriched by industrial processes, …

Crushing Behavior Of Crash Boxes With Hybrid Honeycomb–Auxetic Fillers: Effects Of Architecture, Geometry, And Material Behaviors, A. Yudhanto, A. Jusuf, L. D. Lumanauw, M. Falyanzhuri, A. Afdhal

Mechanical and Aerospace Engineering Faculty Research & Creative Works

Additively manufactured (AM) fillers provide new opportunities to tailor the crushing response and energy absorption of thin-walled metallic crash boxes. This study presents a combined experimental–numerical investigation of hexagonal AA6063-T4 crash boxes filled with architected structures, i.e., honeycomb, auxetic-reentrant, and hybrid honeycomb–auxetic topology. The hybrid configuration, which integrates cells with positive and negative Poisson's ratios, triggers coordinated mechanisms (that enhance folding behavior and collapse control) unattainable via single topology. Quasi-static axial compression tests were conducted to characterize force–displacement curves, deformation mechanisms, energy absorption (EA), and specific energy absorption (SEA). Finite element models developed in the explicit solver LS-DYNA were employed …

Electrochemical Behavior Of Passive Film On Electron Beam Powder Bed Fusion Fabricated Ti-6al-4v Alloy Under Uniaxial Tensile Stress, Hongyu Zheng, Mengdi Yan, Yun Bai, Qian Wang, Lai-Chang Zhang, Jin Xu, Hao Wang, Yingjie Ma, Shujun Li, Rui Yang

Research outputs 2022 to 2026

As medical titanium alloys inevitably undergo synergistic actions of stress and corrosion in human body, a comprehensive investigation of their relevant properties is deemed essential for ensuring their safe application. This work investigates the uniaxial tensile stress electrochemical behaviour of Ti-6Al-4V alloy fabricated by electron beam powder bed fusion under simulated implantation conditions. The results indicate that tensile stress facilitates rapid formation of a dense passive film, which contains a high proportion of high-valent oxides. As the passivation process proceeds, applied stress promotes more defects accumulation in the passive film, compromising its barrier function and reducing corrosion resistance. Under sustained …

Lrq-Solver: A Transformer-Based Neural Operator For Fast And Accurate Solving Of Large-Scale 3d Pdes, Peijian Zeng, Guan Wang, Haohao Gu, Xiaoguang Hu, Tiezhu Gao, Zhuowei Wang, Aimin Yang, Xiaoyu Song

Electrical and Computer Engineering Faculty Publications and Presentations

Solving large-scale PDEs on complex three-dimensional geometries remains a central challenge in scientific and engineering computing, often due to expensive pre-processing stages and high computational overhead. We present Low-Rank Query-based PDE Solver (LRQ-Solver), a physics-integrated deep learning framework for efficient CAE simulations of complex three-dimensional geometries in CAD-driven design analysis. Built upon the Parameter-Conditioned Lagrangian Modeling (PCLM) that embeds physical consistency into the learning process and the Low-Rank Query Attention (LR-QA) module that reduces attention complexity from O(N2) to O(NC2+C3) via covariance decomposition, LRQ-Solver supports multi-configuration analysis within iterative design workflows. On two benchmark datasets, it achieves a 28.6% error …

Mind The Hazard: Modeling And Interpreting Comfort With Personalized Sensing, Yufei Zhang, Matteo Favero, Patrick Chwalek, Sailin Zhong, Denis Lalanne, A. Joseph Paradiso, Clayton Miller, Andrew Sonta

Research Collection College of Integrative Studies

Recent advances in personalized sensing and comfort feedback have spurred the development of data-driven comfort models tailored to individual needs. However, because current models treat sequential comfort feedback independently, they are subject to unstable predictions and limited interpretability, hindering their deployment in building management. This study introduces a dynamic modeling framework that utilizes a Neural Ordinary Differential Equations-based Continuous-time Markov Chain to model the transitions in comfort states over time. Our modeling approach, developed through a field study utilizing smart glasses and mobile app feedback, tracks occupants' comfort transitions across daily activities and contexts. The results demonstrate that this model …

Machine Learning Local Potentials For Accelerated Electron-Phonon Interactions Calculations Within The Projector Augmented-Wave Framework, Yi Xia

Mechanical and Materials Engineering Faculty Publications and Presentations

Electron–phonon interactions govern carrier dynamics, transport, and many optical and quantum phenomena in solids, but finite-displacement calculations within the projector augmented-wave (PAW) framework require up to 6N self-consistent supercell calculations for an N-atom system, making them costly for large, low-symmetry, and disordered materials. We introduce MLLocP, a machine learning (ML) strategy that learns the self-consistent local potential (LocP) on real-space grids and supplies its displacement derivatives to the evaluation of all-electron PAW electron–phonon matrix elements. Analysis of the PAW decomposition identifies the local-potential contribution as a natural target for machine learning, while the remaining PAW quantities are retained within the …

Sinergia Para Un Futuro Sostenible. Tecnologías Y Agrosistemas, Jaime Eduardo Andrade Ramírez, Angélica Bravo Bojórquez

Ingeniería

Este libro reúne los resultados más relevantes del proyecto de investigación “Laboratorio Experimental de Sistemas Tecnológicos Orientados a Modelos Acuapónicos (Lestoma)” desarrollado en la Universidad de Cundinamarca, Extensión Facatativá, liderado por el Grupo de Investigación de Sistemas y Tecnología Facatativá (GISTFA). Lestoma también contó con la colaboración de los grupos de investigación Axioma, Agrociencia y Cundinamarca Agroambiental, adscritos respectivamente al programa de Ingeniería de Sistemas y Computación, Ingeniería Agronómica e Ingeniería Ambiental de las Facultades de Ingeniería y Ciencias Agropecuarias. El proyecto también contó con la colaboración estratégica del grupo de investigación Centro de Investigaciones Ambientales de la Universidad del …

Llm-Driven Simulation Method For Mobilization Decision-Making In Weapons And Equipment Supply Chain, Qichang Guo, Jihai Zhang, Zichu Qin

Journal of System Simulation

Abstract: A LLM-driven multi-agent simulation model for production mobilization in the weapons and equipment supply chain is developed to address the practical challenge of transitioning the supply chain from peacetime to wartime production in response to surging wartime demand. The model consists of a military demand layer, a supply chain execution layer, and a mobilization decision-making layer. An LLM is incorporated as a mobilization decision generation module to represent the dynamic regulation of production conversion and expansion under war-readiness budget and production safety constraints. The simulation results show that compared with the no-mobilization and rule-based mobilization strategies, the LLM-based strategy …

Chemical Plant Control Based On Dynamic Simulation And Reinforcement Learning, Yuxin Li, Haijie Li, Shangbo Han, Kai Gao, Qingyuan Zhang, Yanwen Xiao, Zhengcai Liu

Journal of System Simulation

Abstract: An intelligent control strategy framework combining offline training and on-site deployment for regenerative thermal oxidizer (RTO) operation control based on RL is proposed to solve the problem that fluctuations in exhaust gas concentration and flow, as well as changes in working conditions, make it difficult for RTO to maintain the system operating parameters within the economically optimal range in practical applications. In the offline phase, RL is employed to enable autonomous policy learning and global optimization. In the online phase, an adaptive policy correction mechanism based on real-time data is introduced. RL control strategy is integrated with the operation …

Five-Dimensional Digital Twin System For Closed-Loop Gasoline Blending Optimization, Yaning Fang, Renchu He

Journal of System Simulation

Abstract: An adaptive blending optimization system based on five-dimensional digital twin theory is designed and developed to address the challenges of strict nonlinear constraints and frequent raw material fluctuations. A five-dimensional model architecture is constructed to achieve virtual-real mapping and synergy throughout the blending process. A closed-loop control strategy of "online model correction and dynamic recipe optimization" is proposed. The constrained recursive least squares (RLS) algorithm is adopted to update the octane blending effect model parameters, resolving the model mismatch caused by raw material fluctuations. The linear decreasing weight particle swarm optimization algorithm (LDW-PSO) is combined to determine the optimal …

Research On Digital Shipyard Design-Procurement Coupling Based On Multi-Agent Systems, Kaitai Peng, Dingyi Feng, Wenbo Zhao, Feier Chen

Journal of System Simulation

Abstract: To address the issues of low supply chain collaboration efficiency and inadequate dynamic scheduling capability caused by the disconnection between design and procurement in digital shipbuilding, a method integrating multi-agent simulation with the Q-learning reinforcement learning algorithm is proposed, and a four-tier dynamic supply chain model coupling design and procurement is developed, covering mines, steel mills, shipyards, and shipowners. Based on the depth of collaboration, steel mills are classified into three levels, including full collaboration, partial collaboration, and noncollaboration, with differentiated state spaces and reward functions designed to simulate intelligent pricing and game behaviors under information asymmetry. Simulation results …

Mission-Oriented Combat Effectiveness Evaluation Model For Intelligent Equipment System-Of-Systems, Long Zhang, Wenbo Huang, Xuanming Feng, Zhen Lei, Ying Wang

Journal of System Simulation

Abstract: A hierarchical evolutionary adaptive learning (HEAL) framework is proposed to address the problems in the operational simulation of intelligent equipment systems under complex adversarial environments, including fragmented hierarchical decision-making, unclear mapping between algorithmic indicators and operational effectiveness, and the lack of adaptive evolution capability of simulation models. A multi-layer learning architecture that integrates robust autonomous decision-making for single units, emergent collaboration of system networks, and game-theoretic evolution of system-of-systems strategies is constructed by following the “micro-meso-macro” system-oriented hierarchical modeling paradigm. A cross-layer closed-loop coupling among learned policies, cooperative structures, and operational effectiveness is achieved by combining bottom-up behavioral abstraction …

Multimodal Perception-Based Trajectory Planning Method For Uavs In Forest Environments, Dequan Li, Jiahao Jiang

Journal of System Simulation

Abstract: To address the requirements for low-latency local planning and safe obstacle avoidance in UAV forest-fire patrol under dense tree-trunk occlusion and locally invalid depth observations, this paper proposes a learning-based local trajectory planning method that fuses depth images, local occupancy distance maps, candidate-level collision-risk assessment, and pre-execution safety reranking. The method uses depth images, local occupancy distance maps, motion states, and target direction as inputs and generates 15 quintic-polynomial candidate trajectories in parallel using 3 × 5 angular motion primitives. During training, candidate safety costs, ranking supervision, and near-collision risk labels are constructed. During inference, online LiDAR point clouds …

Manifold Alignment And Dual Prediction-Based Adaptive Dynamic Multi-Objective Evolutionary Algorithm, Xuezhi Yue, Chao Xiong, Yanshen Jiang, Zhaolu Guo, Hu Peng

Journal of System Simulation

Abstract: To address prediction failure, manifold topology distortion, and insufficient adaptability in environmental response and operator configuration in existing prediction-based algorithms for dynamic multi-objective optimization under complex nonlinear environments and small-sample settings, this paper proposes a manifold alignment and dual prediction-based adaptive dynamic multi-objective evolutionary algorithm, termed MADP-DMOEA. When the environment changes, the algorithm decomposes the decision-variable manifold into center and shape components, effectively aligns manifolds at adjacent time instants via bipartite optimal matching, and identifies environmental change severity using geometric displacement. A dual prediction model is developed, in which a GRNN performs center extrapolation under severe changes, while an …

Cooperative Hunting Method For Multiple Usvs Based On Improved Maddpg, Yonghao Zhang, Changyou Xue, Xueman Fan, Zhuo Cheng

Journal of System Simulation

Abstract: A cooperative hunting method based on improved multi-agent deep deterministic policy gradient (MADDPG) is proposed to address the challenges of non-stationary environments, sparse rewards, and partial observations in the cooperative hunting of targets with unknown motion patterns by multiple unmanned surface vessels (USVs). A Markov game model for hunting is built, and a multidimensional guided reward shaping function incorporating collision avoidance, hunting success, and energy consumption penalties is designed to mitigate the sparse reward problem. ResNet residual networks are introduced to construct the Actor and Critic networks and thus enhance the representation capability of deep networks for complex state-action …

Five-Axis Multi-Station Process Planning Based On Multi-Task Graph Reinforcement Learning, Junjie Liu, Jianlei Xu, Litao Ju, Yanhai Hu, Qinghao Zhang, Qiang Liu

Journal of System Simulation

Abstract: A hierarchical intelligent planning architecture integrating multi-task graph and masked proximal policy optimization (MTG-MPPO) is proposed to address the challenges of factorial expansion of the state space and multi-objective interference in five-axis multi-feature machining. An attributed adjacency graph integrating boundary representation and machining direction vectors is constructed. Probabilistic soft labels are output through a multi-task network to quantify process flexibility and thus achieve prior topological pruning of solution space. An MDP model integrating directed acyclic graph masks and dense reward shaping is built. A Monte Carlo (MC) optimization mechanism based on policy distribution is introduced to coordinate cutting, tool-changing, …

Dynamic Multi-Objective Optimization Algorithm Based On Residual-Driven Two-Stage Prediction And Its Application, Xin Zhang, Han Wang, Pengjiang Qian, Wei Fang

Journal of System Simulation

Abstract: A dynamic multi-objective optimization algorithm based on residual-driven two-stage prediction (RDTP) is proposed to address the dilemma of prediction strategies in dynamic multi-objective optimization struggling with insufficient samples in the early stage and low accuracy in the later stage. In the initial stage, if an environmental change is detected, grey prediction, which is fast and suitable for small samples, is employed to generate individuals for the new environment. A residual analysis is performed to determine whether to switch to a LSSVM to improve prediction accuracy as more samples become available. The Pareto front is clustered to select detection objects. …

Optimization Of Mobile Power Resource Allocation And Scheduling For Multi-Point Emergency Response, Ding Ma, Tao Zhang, Bo Jiang, Yajie Liu, Shuijia Li

Journal of System Simulation

Abstract: In extreme event scenarios, urban mobile power resources (MPRs) suffer from uneven spatial distribution and capacity mismatch during emergency scheduling. To achieve globally rational allocation and improve resource configuration and power supply guarantee, a two-stage decision-making method for MPR allocation and scheduling is proposed for multi-point emergency power demands. First, the characteristics of MPRs and travel energy consumption are modeled. In the first stage, an allocation model considering demand satisfaction, capacity matching, and dispatch time is established to solve the macro-level matching problem between MPRs and demand points. In the second stage, an interface scheduling model is constructed based …