Traditional Engineering Research Articles

Magnesium Alloy and Silicon Nitrides Composite Study: Synthesis and Abrasive Water Jet Machining Behavior and Optimization

<div>Related to traditional engineering materials, magnesium alloy-based composites have the potential for automobile applications and exhibit superior specific mechanical behavior. This study aims to synthesize the magnesium alloy (AZ61) composite configured with 0 wt%, 4 wt%, 8 wt%, and 12 wt% of silicon nitride micron particles, developed through a two-step stir-casting process under an argon environment. The synthesized cast AZ61 alloy matrix and its alloy embedded with 4 wt%, 8 wt%, and 12 wt% of Si<sub>3</sub>N<sub>4</sub> are subjected to an abrasive water jet drilling/machining (AJWM) process under varied input sources such as the diameter of the drill (D), transverse speed rate (v), and composition of AZ61 composite sample. Influences of AJWM input sources on metal removal rate (MRR) and surface roughness (Ra) are calculated for identifying the optimum input source factors to attain the best output responses like maximum MRR and minimum Ra via analysis of variant (ANOVA) Taguchi route with L16 design approach. The ANOVA analysis revealed that D, v and the composition of AZ61 alloy composite contribute 26.45%, 16.28%, and 20.84%, respectively, to the output response conditions for higher MRR. Additionally, design 7 exhibits a high MRR of 0.017 g/s and a surface roughness (Ra) of 0.84 μm. The optimum AWJM input source of design 7 is proposed for industries to mass production applications.</div>

ZnO/Organic superlattice with phase composite structure for enhanced thermoelectric performance at low temperature

Semiconducting metal oxides, such as zinc oxide (ZnO), are gaining recognition for thermoelectric applications due to their temperature stability, availability, eco-friendliness, and cost-effectiveness. However, ZnO faces challenges in achieving high ZT value due to its low carrier concentration and high thermal conductivity. Traditional methods, like doping and defect engineering, have shown limited success in overcoming these challenges. In this study, we introduce a unique superlattice structure with a phase-composite composition that significantly decreases thermal conductivity through enhanced phonon scattering while maintaining the power factor by inducing new resonant conducting states near the mobility edge. By optimizing nanolayer thickness and doping concentration, we achieved a remarkable power factor of 14.6 μW cm−1 K−2 and reduced thermal conductivity to ∼1.97 W m−1 K−1 at room temperature in samples with 6 nm-thick ZnO nanolayers fabricated at 100 °C. This leads to a ZT value of ∼0.22 at 300 K, the highest among metal oxide thermoelectric materials at low temperatures, which further increases to ∼0.55 at 510 K. These findings demonstrate the potential of hybrid superlattices for efficient low-temperature thermoelectric applications.

IMPLEMENTING FLOOD-RESISTANT DESIGN STRATEGIES IN COASTAL REGIONS

This systematic review synthesizes findings from 85 articles to examine flood-resistant design strategies in coastal regions, emphasizing an integrated approach that combines traditional engineering solutions, nature-based strategies, green infrastructure, adaptive design, and predictive modeling. The review identifies a continuing reliance on engineered defenses, such as seawalls and automated floodgates, which have proven effective but are limited in addressing the escalating impact of climate change. Findings highlight the growing importance of nature-based solutions—wetlands, mangroves, and coral reefs—that not only mitigate flood risks but also enhance biodiversity and long-term sustainability. Green infrastructure, including permeable pavements and rain gardens, emerges as a valuable urban solution, although it faces implementation challenges in densely populated areas. The integration of predictive modeling and real-time data is transforming flood management by enabling proactive and adaptive responses, though these technologies remain financially inaccessible for many regions. Comparative analysis underscores that flood-resilient design is highly context-dependent, with successful practices varying based on local geography, resources, and socio-economic factors. This review concludes that future flood-resilient infrastructure must prioritize adaptable, sustainable, and region-specific solutions, highlighting the need for cross-disciplinary collaboration and continued policy support to enhance flood resilience and protect vulnerable coastal communities against the effects of climate change.

Enhancing Climate Resiliency Through Improving Ecosystem Services in Shoreline Municipalities – Lessons from Canada

The accelerated impacts of climate change in waterfront areas and the proven inefficacy of the aging hardened shoreline infrastructure have driven shoreline management practices to evolve toward the enhancement of ecosystem services at the land-water interface. Gaining momentum as an adaptive approach in regenerative projects, living shorelines are comprised of natural ecosystem components used in combination or in place of traditional hard engineering methods to provide coastal protective services and erosion mitigation. The success of living shorelines in protecting shoreline property and ecosystem integrity varies based on the biogeomorphology and hydrology of the region and is also heavily reliant on social acceptance of the chosen approach and best practice for implementation. The relatively lower lifecycle cost and associated co-benefits of living shorelines have well positioned them as a promising alternative approach in theory. There are, however, gaps in regional long-term datasets and evidence-based guidelines. This research provides an overview of the underlying geopolitical readiness for integrating nature-based solutions in climate adaptation strategies within shoreline municipalities based on a comprehensive literature review complimented by expert interviews. The synthesized data can inform decisions for minimizing the destructive effects of traditional shoreline erosion prevention approaches and encourage successful implementation of solutions that offer ecological, health, social, and economic benefits.

Chemically Recyclable, Reprocessable, and Mechanically Robust Reversible Cross-Linked Polyurea Plastics for Fully Recyclable Aramid Fiber Reinforced Composites.

Aramid fiber reinforced composites (AFRCs) have received increasing attention because of their excellent comprehensive performance including high mechanical strength, high modulus, and light weight. However, full recycling of AFs from ARCFs is difficult to achieve. Herein, fully recyclable ARCFs are fabricated using reversible cross-linked polyurea plastics (PUHA) as the matrix. PUHA plastics are fabricated by cross-linking linear polyurea using hemiaminal groups. By changing the main chain structures, two types of PUHA plastics are prepared with excellent mechanical performance, which is comparable to that of traditional engineering plastics. PUHA plastics can be reprocessed at least five times without losing their original mechanical properties because of the dynamic exchangeability of the hemiaminal groups. Meanwhile, PUHA plastics can be rapidly depolymerized into linear polyurea under acidic conditions. When PUHA plastics are used as a matrix to fabricate AFRCs, the AFRCs exhibit excellent mechanical strength. Moreover, due to the simple chemical recycling ability of PUHA plastics, AFRCs can be fully decomposed into intact AFs and linear polyurea with high purity. This work presents the use of reversible cross-linked polyurea plastics in the fabrication of fully recyclable AFRCs and provides the future direction of developing fully recyclable and high-performance fiber-reinforced composites.

Digital Grading Company: Deep Neural Networks for Predictive Analytics for Alternative Asset Portfolio Management

In this study, we investigate the application of deep neural networks for predictive analytics in the realm of alternative asset portfolio management, focusing on Trading Card Games (TCGs) like Pokemon. Our primary objective is to understand the optimal trading cards for investment and to determine the appropriate timing for divestment and retention of these assets. Drawing on the successful implementation of machine learning predictive analytics in traditional financial markets, we aim to extend these techniques to alternative investments. To achieve this, we employ a two-pronged approach using a comprehensive dataset of historical sales values for Pokemon TCGs spanning five years, comprising over 200 million unlabeled data points. The first approach utilizes a semi-supervised sequence learning method, where a Long Short-Term Memory (LSTM) model is initially trained as a sequence autoencoder. The pre-trained model's weights are then used in a supervised phase to fine-tune the predictions for future trading card values. The second approach involves heuristic pretraining, which leverages market-derived indicators from existing literature to bridge the gap between traditional feature engineering and deep learning methods. Our technology stack is designed to handle end-to-end data collection, analytics generation, and execution of recommended trading strategies. Simulated backtesting results demonstrate that our Digital Grading Company (DGC) portfolio model outperforms traditional indices, achieving a 357% return compared to the 61% return of the S&P 500 Index over the same period. Real-time analysis further indicates that our model consistently identifies profitable trading opportunities in the TCG market using hourly data and proprietary algorithmic trade logic, executing trades 24/7. The DGC Predictive Portfolio, based on ungraded Pokemon TCG trading cards, also shows superior performance when compared to the Nasdaq 100 Index (Invesco QQQ ETF). Our analytics framework not only predicts the direction of price movement but also provides insights into the magnitude of changes, enabling a more dynamic and responsive trading strategy. The findings suggest that the integration of deep learning models with heuristic indicators has the potential to revolutionize portfolio management in alternative investments, paving the way for more accurate and profitable decision-making in niche markets. This research highlights the promising role of machine learning in enhancing alternative asset management strategies, suggesting future applications in other asset classes. We conclude that deep neural networks, when combined with data-driven heuristics, can significantly enhance trading outcomes, making them valuable tools for investors in the evolving landscape of alternative investments.

The Comparison of Performance of Root Vetiver Grass Between Coconut Fiber and Eggshell Toward Slope Stabilization

Slope stability using vegetation has emerged as a more environmentally friendly and sustainable solution for slope stabilization, replacing traditional structural engineering methods and shotcrete cover. However, despite the application of Vetiver grass, failures in the form of deformation, cracks, and slope collapse still occur. The main objectives of this study are to identify the root properties and analyze the mechanical properties of Vetiver grass with coconut fiber and eggshell fertilizers. Laboratory testing was conducted to determine the mechanical properties of Vetiver grass roots using shear box and universal testing machine. As a result, the root diameter and root length with egg shell exhibit larger values than coconut fiber for 21 days (0.40 – 1.27 cm) and 35 days (11.5 – 61.4 cm). The direct shear test results for Vetiver root with eggshell showed highest shear strength which is 43.67 kN/m2 (21 days) and 168.17 kN/m2 (35 days) compared to Vetiver root with coconut fiber which is 42.94 kN/m2 (21 days) and 61.13 kN/m2 (35 days). For tensile strength, the Vetiver root with egg shell consist higher value which is 0.066 N/mm² (group 1) and 0.068 N/mm² (group 2) compared to Vetiver root with coconut fiber which is 0.059 N/mm² (group 1) and 0.060 N/mm² (group 2). Additionally, in group 1 and 2 of 35 days, eggshell fertilizer showed higher maximum tensile strength, measuring 0.071 N/mm² and 0.094 N/mm², respectively. While, coconut fiber fertilizer 1 and 2 recorded lower tensile strengths of 0.063 N/mm² and 0.062 N/mm², respectively. The significance of this study indicates that the eggshell fertilizers have greater impact towards Vetiver root than coconut fiber fertilizers and without fertilizer.

Error analysis and accuracy evaluation method for coordinate measurement in transformed coordinate system

Multiple coordinate system transformations are required for engineering measurements of multi-faceted components. Traditional engineering measurements neglect coordinate errors after transformations, leading to inaccurate accuracy evaluations for coordinates. In this study, the effects of angular deviation and coordinate value magnitude on coordinate errors during transformations are first analyzed. Additionally, the transformation deviation of the three-point method is examined. Subsequently, an accuracy evaluation method for coordinate measurements is proposed to determine the scale scaling factor and the formula for calculating the variance of measuring coordinate errors. Finally, a coordinate accuracy evaluation method and evaluation formula that consider coordinate system transformation errors are proposed by self-checking. The validity of the proposed accuracy evaluation formula is confirmed through experiments. The coordinate measurement accuracy of the low-cost binocular camera is higher than that of the total station, whether or not coordinate system transformations are considered. Stereo-vision measurement technology is more suitable for small-scale engineering measurements.

Convolutional neural network approach for fault detection and characterization in medium voltage distribution networks

Power outages significantly impact the power industry by disrupting social welfare and economic stability. Still, existing methods for fault detection face challenges due to load and network topology, conditions, and installed equipment. However, recent advances in artificial intelligence (AI) are enabling researchers to create alternative approaches for fault detection and location strategies. Therefore, this paper introduces a novel method for detecting, classifying, and locating faults in power systems through voltage waveform analysis using a convolutional neural network (CNN) integrated with the Piecewise Function Put Together (PFPT) algorithm for fault detection and fault zone localization in a power distribution network. Utilizing Park's transformation, noise reduction PFPT sine fitting, and CNNs, the proposed method distinguishes between 'healthy' and 'faulty' conditions. Simulation results reveal that while the voltage Park's vector time behavior of a healthy system remains stable, it exhibits circular or mixed patterns under faulty conditions. These patterns enable the identification of four types of short circuit faults—single-line-to-ground (LG), line-to-line (LL), line-to-line-to-ground (LLG), and three-line (3L) faults—by analyzing 3D voltage Park's waveforms at network buses. The study validates fault type identification through the observation of rotating Park vectors from sine fitting of time-based voltage waveforms. By converting 3D voltage waveforms into high-resolution images, the method utilizes a CNN for fault recognition, achieving an accuracy of 93.1%. This innovative approach underscores the robustness and precision of combining traditional electrical engineering techniques with modern AI.

Identifying defects and varieties of Malting Barley Kernels

This study introduces a comprehensive approach for classifying individual malting barley kernels, involving dual-sided kernel imaging, a specifically designed image processing algorithm, an optimized deep neural network architecture, and a mechanical sorting system. The proposed method achieves precise classification into multiple classes, aligning with quality standards for malting material assessment. Throughout the study, various image analysis techniques were assessed, including traditional feature engineering, established transfer learning deep neural network architectures, and our custom-designed convolutional neural network tailored for barley kernel image analysis. Comparative analysis underscores the superior performance of our network model. The study reveals that our proposed deep learning network achieves a 94% accuracy in classifying barley kernel defects and varieties, outperforming well-established transfer learning models to complex architectures that attain 93% accuracy. Additionally, it surpasses the traditional machine learning approach involving feature extraction and support vector machine classifiers, which achieve accuracy below 90% in detecting defective kernels and below 70% in varietal classification. However, we also noted the traditional approach’s advantage in morphological feature recognition. This observation guides new research toward integrating morphological feature extraction techniques with modern convolutional networks. This paper presents a deep neural network designed specifically for the analysis of cereal kernel images in two applications: defect and variety classification. It emphasizes the importance of standardizing kernel orientation and merging images from both sides of the kernel, and introduces a device for image acquisition that fulfills this need.

The 129 strain-derived passenger mutations in ACKR1-deficient mice alter the expression of PYHIN and Fc-gamma receptor genes.

A majority of genetically modified mice have been produced using 129 strain-derived embryonic stem cells (ESCs). Despite ample backcrosses with other strains, these may retain characteristic for 129 passenger mutations leading to confounding phenotypes unrelated to targeted genes. Here we show that widely used Ackr1-/-129ES mice have approximately 6Mb of the 129-derived genome retained adjacently to the Ackr1 locus on chromosome 1, including several characteristic polymorphisms. These most notably affect the expression of PYHIN and Fc-gamma receptor genes in myeloid cells resulting in the overproduction of IL-1β by activated macrophages and the loss of Fc-gamma receptors on myeloid progenitor cells. Therefore, caution is warranted when interpreting Ackr1-/-129ES mouse phenotypes as being solely due to the ACKR1 deficiency. Our findings call for a careful reevaluation of data from previous studies using Ackr1-/-129ES mice and underscore the limitations and pitfalls inherent to mouse models produced using traditional genetic engineering techniques involving 129 ESCs.

Assessing benefits of computer-based video training and tools on learning outcomes and motivation in mechanical engineering education: digitalized intervention and approach

Traditionally, laboratory work has been a common approach to facilitate the acquisition of practical skills and familiarize the undergraduate students, particularly engineering students, with specialized tools and equipment. However, the conventional in-person labs often experience challenges such as limited resources and space, instructor availability, and inflexible schedules. The emergence of digital tools and the recent COVID-19 pandemic have prompted educators to reconsider their teaching methods. To this effect, this paper introduces an innovative approach and teaching methodology to address the challenges in traditional engineering education within the laboratory. It presents a methodology that combines cost-effective instructional videos and portable kits to promote autonomous development of practical skills in undergraduate engineering students. The proposed teaching methodology was grounded on the Descriptive Decision theory and Learning-Oriented Assessment (LOA), which are theoretical framework and type of students’ learning outcome (SLO) model that studies the rationality behind the decisions that the users are disposed to make, as well as level of outcome of the students’ learning process or performance. Motivation among the students was assessed using the Model of Academic Motivation Inventory (MUSIC Inventory) to evaluate the impact of the proposed teaching method and learning intervention on the interest of the students. The method was implemented in four independent courses at two different campuses of Tecnologico de Monterrey. The results show that the proposed learning approach was effective in helping students develop hard skills with reduced instructor intervention. Moreover, high levels of motivation was reported through the MUSIC methodology and test administered to the participating students at the end of the courses. The outcome of this study can be used to inform and support the curriculum design by the educators, promote effective policy and decision making by the university leaders, and encourage wide adoption of the digitalized-education.

Intelligent control of the Magnus anti-rolling device: A co-simulation approach

In this study, we present an artificial intelligence control method specifically designed for the Magnus anti-rolling device. The core of our approach is the development of a co-simulation framework that integrates an intelligent algorithm with a three-dimensional numerical computational programme within a complex hydrodynamic environment. This integration is achieved through the use of a deep reinforcement learning algorithm, which allows for intelligent adaptation of the anti-rolling device's rotating speed in real time. Our research focuses on evaluating the performance of the intelligent anti-rolling control algorithm under a variety of conditions, including different ship-model roll angles, column geometry models, and varying swinging or slewing speeds. Through numerical studies, we compare the effects of these variables on the effectiveness of the control method. The co-simulation technology provides a platform for testing the intelligent control method. It allows a detailed examination of how the intelligent algorithm interacts with the hydrodynamic responses of the Magnus anti-rolling device, ensuring that the control method can adapt to a wide range of operational scenarios. This adaptability is crucial for maintaining stability and improving the performance of the anti-rolling device in real maritime environments. This study highlights the potential for integrating artificial intelligence with traditional maritime engineering solutions.

Recent advances of CRISPR-based genome editing for enhancing staple crops.

An increasing population, climate change, and diminishing natural resources present severe threats to global food security, with traditional breeding and genetic engineering methods often falling short in addressing these rapidly evolving challenges. CRISPR/Cas systems have emerged as revolutionary tools for precise genetic modifications in crops, offering significant advancements in resilience, yield, and nutritional value, particularly in staple crops like rice and maize. This review highlights the transformative potential of CRISPR/Cas technology, emphasizing recent innovations such as prime and base editing, and the development of novel CRISPR-associated proteins, which have significantly improved the specificity, efficiency, and scope of genome editing in agriculture. These advancements enable targeted genetic modifications that enhance tolerance to abiotic stresses as well as biotic stresses. Additionally, CRISPR/Cas plays a crucial role in improving crop yield and quality by enhancing photosynthetic efficiency, nutrient uptake, and resistance to lodging, while also improving taste, texture, shelf life, and nutritional content through biofortification. Despite challenges such as off-target effects, the need for more efficient delivery methods, and ethical and regulatory concerns, the review underscores the importance of CRISPR/Cas in addressing global food security and sustainability challenges. It calls for continued research and integration of CRISPR with other emerging technologies like nanotechnology, synthetic biology, and machine learning to fully realize its potential in developing resilient, productive, and sustainable agricultural systems.

Understanding Cytochrome P450 Enzyme Substrate Inhibition and Prospects for Elimination Strategies.

Cytochrome P450 (CYP450) enzymes, which are widely distributed and pivotal in various biochemical reactions, catalyze diverse processes such as hydroxylation, epoxidation, dehydrogenation, dealkylation, nitrification, and bond formation. These enzymes have been applied in drug metabolism, antibiotic production, bioremediation, and fine chemical synthesis. Recent research revealed that CYP450 catalytic kinetics deviated from the classic Michaelis-Menten model. A notable substrate inhibition phenomenon that affects the catalytic efficiency of CYP450 at high substrate concentrations was identified. However, the substrate inhibition of various reactions catalyzed by CYP450 enzymes have not been comprehensively reviewed. This review describes CYP450 substrate inhibition examples and atypical Michaelis-Menten kinetic models, and provides insight into mechanisms of these enzymes. We also reviewed 3D structure and dynamics of CYP450 with substrate binding. Outline methods for alleviating substrate inhibition in CYP450 and other enzymes, including traditional fermentation approaches and protein engineering modifications. The comprehensive analysis presented in this study lays the foundation for enhancing the catalytic efficiency of CYP450 by deregulating substrate inhibition.

Competency requirements for engineering management undergraduates in the era of intelligent construction

ABSTRACT In the era of intelligent construction, engineering management requires diverse abilities that cannot be provided through traditional engineering management education. Moreover, few studies focused on the competency requirements for engineering management undergraduates. In this study, a questionnaire with 12 competency indicators, including traditional engineering management competencies and new competencies arising from the advancement of information technology and artificial intelligence (AI), was designed and distributed to 942 employees from enterprises related to engineering construction. A total of 723 responses were received, of which 514 were deemed valid. The survey results indicate that the competency in traditional engineering management is considered “strongly necessary” or “necessary” by 84.24% of respondents. Similarly, competencies in analyzing and processing large-scale construction data, and in assisting feasibility analysis with AI, are valued by 80.35% and 78.99% of respondents, respectively. The above results illustrate that the integration of intelligent construction into traditional engineering management competencies is critical for future engineering management undergraduates. Therefore, courses on intelligent construction, such as data analysis, Building Information Modeling, and the regulated use of large language models, should be included in engineering management education. This study can provide significant support for reforming the education of engineering management.

Optimizing ash content detection and prediction in flotation tailings using a new approach to enhance feature extraction and deep learning algorithms

ABSTRACT Flotation is a crucial separation technique in coal beneficiation. However, the inability to quickly and accurately detect ash content during the flotation process hinders the development of flotation automation. This paper introduced a novel hybrid model based on convolutional neural networks, establishing the ash content in tailings. In this method, convolutional neural network is utilized to automatically extract features from tailings images. In the model training and optimization phase after inputting the augmented data, the optimal ResNet18 model is identified by comparing five model optimization methods and replacing the SoftMax classifier with the traditional SVR in ResNet18. Additionally, a PCA-SVR model for predicting tailings ash content is constructed for comparative analysis, employing six different feature combinations to validate the effectiveness of the proposed features, with optimal support vector parameters identified through random search and cross-validation algorithms. The prediction results show that the PCA-SVR model has the highest accuracy when the feature dimension is reduced to 16, and the prediction of ResNet18-SVR is more accurate compared to PCA-SVR. This indicates that the proposed method can predict the ash content more accurately than the traditional feature engineering methods and has a broad application prospect in the field of coal beneficiation.

Competencies in interdisciplinary engineering education: constructing perspectives on interdisciplinarity in a Q-sort study

ABSTRACT Interdisciplinary engineering education (IEE) is gaining traction as engineering practices increasingly acknowledge the need to transcend traditional boundaries given the complexities of globalised systems. IEE, however, faces challenges that underscore uncertainties and different perceptions about what interdisciplinarity means for engineering education, what it requires, and how current educational practices should change accordingly. This study addresses these concerns by taking competencies as indicators. Analysing new angles interdisciplinary approaches bring to traditional engineering competencies, we first propose six IEE-related competency categories. We then use the Q-sort methodology to investigate how interdisciplinarity is envisioned within overall engineering educational goals. Findings highlight three distinct perspectives, seeing IEE respectively as social-holistic, technical problem-solving, and reflective pragmatic. Each perspective embodies specific values and conceptions about the role and implications of interdisciplinarity for engineering education. The multifaceted understandings of and approaches to realise IEE suggest the need for nuanced strategies and continuous dialogues in education.

Open-Source Data Formalization through Model-Based Systems Engineering for Concurrent Preliminary Design of CubeSats

Market trends in the space sector suggest a notable increase in satellite operations and market value for the coming decade. In parallel, there has been a shift in the industrial and academic sectors from traditional Document-Based System Engineering to Model-based systems engineering (MBSE) combined with Concurrent engineering (CE) practices. Due to growing demands, the drivers behind this change have been the need for quicker and more cost-effective design processes. A key challenge in this transition remains to determine how to effectively formalize and exchange data during all design stages and across all discipline-specific tools; as representing systems through models can be a complex endeavor. For instance, during the Preliminary design (PD) phase, the integration of system models with external mathematical models for simulations, analyses, and system budgeting is crucial. The introduction of CubeSats and their standard has partly addressed the question of standardization and has aided in reducing overall development time and costs in the space sector. Nevertheless, questions about how to successfully exchange data endure. This paper focuses on formalizing a CubeSat model for use across various stages of the PD phase. The entire process is conducted with the exclusive use of open-source tools, to facilitate the transparency of data integration across the PD phases, and the overall life cycle of a CubeSat. The paper has two primary outcomes: (i) developing a generic CubeSat model using Systems modeling language (SysML) that includes data storage and visualization through the application of Unified modeling language (UML) stereotypes, streamlining in parallel information exchange for integration with various simulation and analysis tools; (ii) creating an end-to-end use case scenario within the Nanostar software suite (NSS), an open-source framework designed to streamline data exchange across different software during CE sessions. A case study from a theoretical academic space mission concept is presented as the illustration of how to utilize the proposed formalization, and it serves as well as a preliminary validation of the proposed formalization. The proposed formalization positions the CubeSat SysML model as the central data source throughout the design process. It also supports automated trade-off analyses by combining the benefits of SysML with effective data instantiating across all PD study phases.

GRNN-based cascade ensemble model for non-destructive damage state identification: small data approach

AbstractAssessing the structural integrity of ageing structures that are affected by climate-induced stressors, challenges traditional engineering methods. The reason is that structural degradation often initiates and advances without any notable warning until visible severe damage or catastrophic failures occur. An example of this, is the conventional inspection methods for prestressed concrete bridges which fail to interpret large permanent deflections because the causes—typically tendon loss—are barely visible or measurable. In many occasions, traditional inspections fail to discern these latent defects and damage, leading to the need for expensive continuous structural health monitoring towards informed assessments to enable appropriate structural interventions. This is a capability gap that has led to fatalities and extensive losses because the operators have very little time to react. This study addresses this gap by proposing a novel machine learning approach to inform a rapid non-destructive assessment of bridge damage states based on measurable structural deflections. First, a comprehensive training dataset is assembled by simulating various plausible bridge damage scenarios associated with different degrees and patterns of tendon losses, the integrity of which is vital for the health of bridge decks. Second, a novel General Regression Neural Network (GRNN)-based cascade ensemble model, tailored for predicting three interdependent output attributes using limited datasets, is developed. The proposed cascade model is optimised by utilising the differential evolution method. Modelling and validation were conducted for a real long-span bridge. The results confirm the efficacy of the proposed model in accurately identifying bridge damage states when compared to existing methods. The model developed demonstrates exceptional prediction accuracy and reliability, underscoring its practical value in non-destructive bridge damage assessment, which can facilitate effective restoration planning.