Traditional Engineering Research Articles

Virtual Full Scale Static Test of a Civil Tilt Rotor Composite Wing in Non-Linear Regime

This study addresses the crucial role of post-buckling behavior analysis in the structural design of composite aeronautical structures. Traditional engineering practices tend to result in oversized composite components, increasing structural weight. EASA AMC 20-29’s Building Block Approach suggests phased testing, but its time and cost challenges necessitate a shift to high-fidelity post-buckling analyses, exemplified by MSC NASTRAN SOL 400. This approach, showcased in the analysis of the Next Generation Civil Tilt Rotor Technology Demonstrator’s wing (NGTCTR-TD), effectively de-risks static tests, contributing to a more efficient certification process. The study demonstrates how advanced simulations provide detailed insights into local buckling phenomena, allowing precise stress distribution analysis. These analyses eliminate the risk of structural failure, paving the way for safer, more efficient, and cost-effective airframe structures. Future developments aim to validate numerical analyses with experimental data, further emphasizing the reliability and benefits of high-fidelity simulations.

Improving the hot corrosion resistance of additively manufactured Inconel 718 via recrystallization-based grain boundary engineering induced by its residual stress

Metals containing abundant coherent twin boundaries (TBs) can improve corrosion resistance. Tailoring TBs in additive manufacturing (AM)-fabricated metallic materials with lower stacking fault energy, however, has proven impracticable because the traditional grain boundary engineering (GBE) involved in thermomechanical processing is not appropriate for AM-fabricated alloys with complicated geometric shapes. Here, the AM-fabricated Inconel 718 superalloy was successfully utilized to form abundant TBs induced by the intrinsic residual internal stresses, produced by AM processing via direct heat treatment. The microstructural characteristics and corresponding hot corrosion behaviors of different specimens – initial AM-fabricated, directly aged, and GBE-treated specimens – exposed to a mixed salt of 75 wt% Na2SO4 + 25 wt% NaCl at 650 °C were compared and analyzed systematically. The corrosion mechanisms affecting each of the specimens are discussed in detail. The obtained results reveal that the GBE-treated specimen has the most intact surface after the corrosion test, suffering minimum mass loss and having the shallowest corrosion layer depth, exhibiting optimal hot corrosion resistance. This optimal performance is mainly attributed to the penetration of S and O elements that were efficiently impeded by the TBs. This result holds implications for improving hot corrosion resistance in AM-fabricated metallic materials via directly GBE induced by residual stress.

Integrating artificial intelligence into engineering processes for improved efficiency and safety in oil and gas operations

This paper delves into the significance, challenges, and potential of AI applications within the oil and gas sector. In the dynamic landscape of oil and gas operations, efficiency and safety stand as paramount concerns. Traditional engineering processes, while robust, often face limitations in adapting to the evolving complexities of the industry. However, the advent of AI technologies offers a paradigm shift, presenting unprecedented opportunities for optimization and risk mitigation. This paper explores the multifaceted role of AI in engineering processes throughout the oil and gas value chain. It examines how AI, encompassing machine learning, deep learning, and predictive analytics, empowers decision-makers with real-time insights, optimizing exploration, production, transportation, and refining processes. Efficiency gains are witnessed through predictive maintenance strategies, enabling proactive asset management and minimizing downtime. Additionally, AI-driven process optimization techniques enhance resource allocation, streamlining operations and maximizing output while reducing costs. Moreover, AI's integration fosters a culture of safety by augmenting risk assessment and hazard identification capabilities. Through advanced algorithms, AI systems analyze vast datasets to detect anomalies and predict potential safety hazards, enabling proactive intervention and accident prevention. However, the journey towards AI integration is not without challenges. Technical complexities, regulatory frameworks, and cyber security concerns pose significant hurdles that require careful navigation. Moreover, ethical considerations surrounding data privacy and algorithmic bias necessitate robust governance frameworks to ensure responsible AI deployment. Looking ahead, the paper delineates future trends and opportunities in AI adoption within the oil and gas sector. It underscores the potential for continued innovation and disruption, reshaping workforce dynamics and skill requirements. Embracing AI not only drives operational excellence but also propels the industry towards a sustainable and resilient future

Protection of Gully Slope in Loess Plateau

Rigid protection of traditional engineering slope protection measures can play a certain role in slope protection at the initial stage of the project, but with the passage of time, the slope will be affected by weathering, the stability of the slope will be reduced, in addition, a large area of engineering rock surface exposed will not be coordinated with the surrounding natural vegetation environment, which does not meet the requirements of sustainable development. Therefore, the ecological engineering restoration technology combining engineering measures and biological measures has become the trend and mainstream of slope management and restoration.

Research on design requirements for passive residual heat removal system of lead cooled fast reactor via model-based system engineering

Traditional text-based system engineering, which has been used in the design and application of passive residual heat removal system (PRHRS) for lead-cooled fast reactors, is prone to several problems such as low development efficiency, long iteration cycles, and model ambiguity. This study aims to effectively address the above-mentioned problems by adopting a model-based system engineering (MBSE) method, which has been preliminarily applied to meet the design requirements of a PRHRS. The design process has been implemented based on the preliminary design of the system architecture and consists of three stages: top-level requirement analysis, functional requirements analysis, and design requirements synthesis. The results of the top-level requirements analysis and the corresponding use case diagram can determine the requirements, top-level use cases, and scenario flow of the system. During the functional requirements analysis, the sequence, activity, and state machine diagrams are used to develop the system function model and provide early confirmation. By comparing these sequence diagrams, the requirements for omissions and inconsistencies can be effectively checked. In the design requirements synthesis stage, the Analytic Hierarchy Process is used to conduct preliminary trade-off calculations on the system architecture, after which a white box model is established during the system architecture design. Through these two steps, the analysis and design of the system architecture are ultimately achieved. The resulting system architecture ensures the consistency of the design requirements. Ultimately, a functional hazard analysis was conducted for a specific incident to validate case requirements and further refine the system architecture. Future research can further reduce the design risk, improve the design efficiency, and provide a practical reference for the design and optimization of PRHRS in digital lead-cooled fast reactors.

The value of marsh restoration for flood risk reduction in an urban estuary

The use of nature-based solutions (NBS) for coastal climate adaptation has broad and growing interest, but NBS are rarely assessed with the same rigor as traditional engineering solutions or with respect to future climate change scenarios. These gaps pose challenges for the use of NBS for climate adaptation. Here, we value the flood protection benefits of stakeholder-identified marsh restoration under current and future climate change within San Francisco Bay, a densely urbanized estuary, and specifically on the shores of San Mateo County, the county most vulnerable to future flooding in California. Marsh restoration provides a present value of $21 million which increases to over $100 million with 0.5 m of sea level rise (SLR), and to about $500 million with 1 m of SLR. There are hotspots within the county where marsh restoration delivers very high benefits for adaptation, which reach $9 million/hectare with likely future sea level and storm conditions. Today’s investments in nature and community resilience can result in increasing payoffs as climate change progresses and risk increases.

Low-cycle fatigue design for reinforced high-strength concrete under high compressive stress

As engineering endeavors push the boundaries of material and design capabilities, the significance of understanding and mitigating fatigue in construction materials becomes paramount. This study specifically investigates the low-cycle fatigue performance of reinforced high-strength concrete (RHSC). Using rigorous data collection, we established a clear link between interpretable machine learning analysis and the fatigue properties of RHSC. A trained model was developed, yielding a straightforward formula tailored to low-cycle fatigue design considerations for RHSC. This model stands as a testament to the potential for marrying traditional engineering practices with advanced statistical techniques. Our results emphasize that, when appropriately applied, regression analysis can be instrumental in enhancing the durability and longevity of RHSC structures exposed to dynamic loadings. This research not only underscores the pivotal role of statistical methods in fatigue design but also champions the broader adoption of such techniques in evolving engineering landscapes.

Nanoenabled Immunomodulatory Scaffolds for Cartilage Tissue Engineering

AbstractArticular cartilage regeneration is a challenge in tissue engineering. Although diverse materials have been developed for this purpose, cartilage regeneration remains suboptimal. The integration of nanomaterials into 3D network materials holds great potential in the improvement of key mechanical properties, particularly important for osteochondral replacement scaffolds and even to function as carriers for disease‐modifying drugs or other regulatory signals. In this study, a simple yet effective cell‐free nanoenabled Col‐PLA scaffold specially designed to enhance cartilage regeneration and modulate inflammatory response is proposed, by incorporating poly(lactic‐co‐glycolic acid) (PLGA) ibuprofen nanoparticles (NPs) into a collagen/polylactide (Col‐PLA) matrix. The developed nanoenabled scaffold successfully decreases IL‐1β release and leads to primary human chondrocytes survival, ultimately restoring extracellular matrix (ECM) production under inflammatory conditions. The nanoenabled Col‐PLA scaffolds secretome effectively decreases macrophage invasion in vitro, as well as neutrophil infiltration and inflammatory mediators’, namely the complement component C5/C5a, C‐reactive protein, IL‐1β, MMP9, CCL20, and CXCL1/KC production in vivo in a rodent air‐pouch model. Overall, the established nanoenabled scaffold has the potential to support chondrogenesis as well as modulate inflammatory response, overcoming the limitations of traditional tissue engineering strategies.

Disaster risk management, climate change adaptation and the role of spatial and urban planning: evidence from European case studies

AbstractThis paper advances knowledge and understanding of the relationships between risk management, climate change adaptation and spatial planning as good territorial governance practices. The aim is to present evidence on how risks and their management are progressively being integrated into national planning systems in order to reduce territorial vulnerability and costs related to natural events in the European context. This paper is based on the ESPON-TITAN project which focuses mainly on flood events that occurred in Rotterdam, Prague, the Po river basin, Pori, Andalucia, Nouvelle-Aquitaine, Dresden and the Alpine region. The paper reviews the literature and planning instruments applied in the selected case studies, as well as interviews with key stakeholders and decision makers. The results confirm the hypothesis that traditional disaster management is evolving towards Disaster Risk Management, clearly recognizing that Climate Change modifies and increases threats. Data on the consequences of natural disasters support the desirability of a proactive rather than a reactive approach, highlighting the crucial role of planning. The resulting governance is more "functional" than "territorial", leaving room for further advances and innovations such as territorial and multi-risk perspective, partnerships and civil society participation, and soft versus traditional hard or engineering solutions.

An Immersive Hybrid Approach to Materials & Solid Mechanics Lab Activities for Undergrad Students

This paper describes two institutions' efforts to provide engineering science students with experiential learning opportunities using low-cost, simple physical lab experiments and its efficacy in improving students perceived understanding levels. A Canadian university developed a "hybrid" lab activity that combined a virtual lab simulator with physical lab experiments to teach materials, solid mechanics, and instrumentation concepts in two different 2nd year undergraduate solid mechanics courses. At a small American college, students in an introduction to materials engineering course completed four individual laboratory exercises using simple and relatively inexpensive material testing setups that explored topics covered in course lectures and readings. Students learned about the behavior of engineering materials and structural analysis using low-cost materials test apparatuses for different loading modes, engaging their senses to aid their understanding. Students then constructed a virtual simulation model using Finite Element Analysis (FEA). Students at both institutions gave positive feedback and reported improved understanding of course topics. The use of low-cost experiments combined with traditional engineering labs shows promise for improving student understanding of engineering science concepts.

Inclusive Learning Through Equity-Driven Approaches to Design in Engineering Education

Despite it being widely acknowledged that integrating principles of equity, diversity, inclusion, and accessibility (EDIA) in engineering has numerous benefits such as more innovative and inclusive design outcomes, there have been ongoing challenges in addressing equity, promoting diversity, and fostering inclusive teaching and learning strategies in higher education. If EDIA is taught separately from coursework in engineering, students are unlikely to engage with or incorporate EDIA principles in their work. Moreover, the pedagogical approaches for EDIA concepts, which include exploratory discussion and reflection, can be a barrier for student learning if they are not valued in the same way as traditional engineering epistemology and ideologies that prioritize a technical space and objective data. Lastly, evidence demonstrates that a lack of sense of belonging for underrepresented students can impact their learning experience. It is important to address how teaching approaches impact the shift of student mindsets in their design work and their engagement with the learning environment. In this exploratory project, we seek to understand the ways in which learning equity-driven approaches to design, such as design thinking, may impact engineering students’ perceptions of inclusivity in their learning environment and the quality of inclusivity in the work that they design and engineer. We focus on the rationale and development of the methodology in this paper since the work is still in progress. We are working with a diverse team of educators and researchers to review and revise course goals, student learning outcomes and related course content for two design thinking courses in a master’s level engineering program. Changes to the course material include further integrating EDIA principles and the “Liberatory Design framework” described by the Hasso Plattner Institute of Design as “a process and practice to liberate designers from habits that perpetuate inequity.” The impact of such revision is examined through qualitative analysis of students’ written reflections with prompts that focus on EDIA themes. A post-course survey is also used to assess students’ perceptions of EDIA in relation to their academic, professional, and personal learning experiences throughout the program and, more specifically, the design thinking course learning outcomes. Preliminary findings suggest that students connect design thinking approaches with an awareness of the value of diversity in their design teams and with more inclusive design outcomes for the end-user. This exploratory work can inspire research to further examine the role of design thinking and related pedagogical approaches in supporting the integration of EDIA principles in teaching engineering design.

Gender Parity and Program Choice: Which Engineering Programs Women Students Prefer to Enter

Many engineering programs have historically struggled to attract and retain women students. At UBC, two new engineering programs—Biomedical Engineering (BME) and Environmental Engineering (ENV)—have a high percentage of women students compared to other disciplines. However, anecdotal evidence consistent with research on US schools suggested that BME and ENV might have drawn women away from more traditional engineering programs, such as mechanical or civil. This creates a challenge for improving gender parity across all engineering disciplines, and means women are over-represented in disciplines that comprise a small proportion of industry jobs. In this paper, we examine 11 years of student data ranking preferences for second-year programs out of a common first-year at UBC. We found evidence that the addition of BME is associated with a drop in the fraction of women selecting electrical engineering. We also found patterns in the combinations of top-ranked disciplines for men and women students. And we examined the impact of UBC’s pre-BME program on ranking BME as a first choice for women and men.

Geospatial-based decision support system for prioritizing road segments for maintenance and rehabilitation

Given the growing demand for moving goods and people and the escalating number of vehicles on highways, issues related to road safety have become common concerns. Additionally, the aging of the road infrastructure and the intensification of climatic change effects reinforce the problem of road pavement serviceability. However, accessing funds for road maintenance and having the autonomy for pure technical decisions remain significant challenges for transport authorities. Thus, this paper addresses developing a geospatial-based systematic process for prioritizing highway segment candidates for maintenance and rehabilitation. The investigation was designed to facilitate the decision-making process for the state transportation authority to allocate funds over the 20.315 km of major highways in Minas Gerais, Brazil. The method consisted of ranking the highway segments regarding the geographic context to which each segment belongs, as well as the operational characteristics of the highways. The approach meets the United Nations Sustainable Development Goals by considering socio-economic and demographic perspectives along with other traditional engineering characteristics such as the available transport infrastructure, the traffic volume, the highway capacity, the pavement serviceability and the potential for deterioration, the condition of the surface drainage, and weather-related data. Transportation practitioners with expertise in pavement maintenance and rehabilitation and transportation managers contributed to the development by weighting the factors according to the best possible technical judgment regarding prioritizing funds. The GIS-oriented Delphi framework resulted in a priority index. The index allowed the elaboration of a ranking of priorities, which is vital information regarding public policies and the transparent use of public money for road maintenance and rehabilitation within the State of Minas Gerais. The model is robust and outputs geoinformation and tabular data to feed the decision-making process in an organized and transparent manner. Therefore, the core methodology can be easily transferable to other jurisdictions.

Restoration of the Elizabeth Tower: pairing tradition and modernity at the Houses of Parliament

What was conceived as a light-touch refurbishment of the Elizabeth Tower (commonly referred to as 'Big Ben') has developed into the most comprehensive restoration in the 160-year history of this iconic Grade I listed building at the Palace of Westminster. During the seven-year life of the project, the rich and complex history of the tower has been slowly revealed, shedding light on the unique structural features of the building, and helping to shape the engineering response to the challenges encountered. Dealing with these has required a wide range of engineering techniques to be applied, from traditional conservation engineering methods to the latest in surveying tools and computer analysis techniques. During the restoration, the roofs – which are constructed wholly of cast iron – were stripped down to the main rafters and every element inspected, repaired and repainted. The clock mechanism was dismantled and sent for refurbishment and the dials restored and reglazed. Weathered stonework was replaced, and external decorations refreshed. A new lift was installed to improve access, alongside upgrade of life-safety systems to 21st century standards.

Estimation of slope stability using ensemble-based hybrid machine learning approaches

Mining is one of the most daunting occupations gain the sector since it entails risk at any point in the operation. In its operation, the main focus is on slope stability. To avoid slope failures, work should be performed in line with both the regulations and the safety criteria. Slope stability is essential in mining activities owing to slope failure putting productivity and safety at risk. Prediction of slope failure is difficult because of the complexity of traditional engineering techniques. Through study, recent technologies have helped mining companies predict slope problems quickly and effectively. In this current research, an ensemble of machine learning intelligence algorithms was used to estimate and assess the Factor of Safety (FOS). In Ostapal Chromicte Mine, India, 79 experimental and failure slope occurrences were tracked to gather in-the-moment field data. The available data were split into training and testing sets at random to build algorithms. The five influenced factors such as the unit weight, the friction angle, the cohesiveness, the mining depth, as well as the slope angle used as input variables to estimate the FOS. Selected machine learning techniques such as Multiple Linear Regression (MLR), Decision Tree, Random Forest (RF), eXtreme Gradient Boosting (XGBoost) and ensemble hybrid model combining eXtreme Gradient Boosting and Random Forest (XGBoost-RF) were developed to evaluate the FOS. The validity and efficiency of created models can be evaluated using standard evaluation parameters such as coefficient of determination (R2), root mean square error (RMSE), mean square error (MSE), normalized root mean square error (NRMSE), mean absolute percentage error (MAPE) and mean absolute deviation (MAD). The most precise model to assess the FOS across all models was discovered to be the XGBOOST-RF ensemble model, which had a high R2 of 0.931, MSE of 0.009, NRMSE of 0.069, MAD of 0.037, MAPE of 3.581 and an RMSE of 0.098.

Student understanding of the Bloch sphere

Quantum information science is a rapidly growing interdisciplinary field that is attracting the attention of academics and industry experts alike. It requires talent from a wide variety of traditional fields, including physics, engineering, chemistry, and computer science, to name a few. To prepare students for such opportunities, it is important to give them a strong foundation in the basics of quantum information science, in which quantum computing plays a central role. In this study, we discuss the development, validation, and evaluation of a tutorial on the Bloch sphere, a useful visual tool for developing intuition about single quantum bits (qubits), which are the basic building block of any quantum computer. Students’ understanding was evaluated after they received traditional lecture-based instruction on the requisite topics, and again after engaging with the tutorial. We observe, analyze, and discuss their improvement in performance on concepts covered in the tutorial.

Image‐based characterization of flocculation processes through PLS inspired representation learning in convolutional neural networks

AbstractMonitoring of flocculation processes such as those used in downstream processing of a fermentation broth is essential for process control. One approach is to apply microscopic imaging combined with image analysis for characterizing the state of the process. In this work, we investigate and compare the use of supervised feedforward convolutional neural network (CNN) architectures to predict the process states from the image information and compare the results with the traditional alternative of characterizing flocs based on manually engineered image features guided by human expertise. From a well‐defined image data set representing six process states, the objective is to establish end‐to‐end classification models which are accurate but at the same time learn meaningful latent variable space representations. Specifically, we evaluate three different CNN architectures with varying degrees of regularization and compare results with logistic regression models based on inputs from two different traditional feature engineering methods. By applying global average pooling as a structural regularizer to the CNN architecture, we significantly improve the generalization performance in comparison with the classification accuracies of the traditional feature engineered models. Furthermore, we show that by imposing a projection to latent structures (PLS) like regularization framework onto the CNN, it can also learn a latent variable representation that mimics the features selected by human expertise.

Discovery of broadly-neutralizing antibodies against brown recluse spider and Gadim scorpion sphingomyelinases using consensus toxins as antigens.

Broadly-neutralizing monoclonal antibodies are becoming increasingly important tools for treating infectious diseases and animal envenomings. However, designing and developing broadly-neutralizing antibodies can be cumbersome using traditional low-throughput iterative protein engineering methods. Here, we present a new high-throughput approach for the standardized discovery of broadly-neutralizing monoclonal antibodies relying on phage display technology and consensus antigens representing average sequences of related proteins. We showcase the utility of this approach by applying it to toxic sphingomyelinases from the venoms of species from very distant orders of the animal kingdom, the recluse spider and Gadim scorpion. First, we designed a consensus sphingomyelinase and performed three rounds of phage display selection, followed by DELFIA-based screening and ranking, and benchmarked this to a similar campaign involving cross-panning against recombinant versions of the native toxins. Second, we identified two scFvs that not only bind the consensus toxins, but which can also neutralize sphingomyelinase activity of native whole venom in vitro. Finally, we conclude that the phage display campaign involving the use of the consensus toxin was more successful in yielding cross-neutralizing scFvs than the phage display campaign involving cross-panning.

A Comparative Study of Machine Learning and Algorithmic Approaches to Automatically Identify the Yield Point in Normal and Aneurysmal Human Aortic Tissues.

The stress-strain curve of biological soft tissues helps characterize their mechanical behavior. The yield point on this curve is when a specimen breaches its elastic range due to irreversible microstructural damage. The yield point is easily found using the offset yield method in traditional engineering materials. However, correctly identifying the yield point in soft tissues can be subjective due to its nonlinear material behavior. The typical method for yield point identification is visual inspection, which is investigator-dependent and does not lend itself to automation of the analysis pipeline. An automated algorithm to identify the yield point objectively assesses soft tissues' biomechanical properties. This study aimed to analyze data from uniaxial extension testing on biological soft tissue specimens and create a machine learning (ML) model to determine a tissue sample's yield point. We present a trained machine learning model from 279 uniaxial extension curves from testing aneurysmal/nonaneurysmal and longitudinal/circumferential oriented tissue specimens that multiple experts labeled through an adjudication process. The ML model showed a median error of 5% in its estimated yield stress compared to the expert picks. The study found that an ML model could accurately identify the yield point (as defined) in various aortic tissues. Future studies will be performed to validate this approach by visually inspecting when damage occurs and adjusting the model using the ML-based approach.

Elk herd optimizer: a novel nature-inspired metaheuristic algorithm

This paper proposes a novel nature-inspired swarm-based optimization algorithm called elk herd optimizer (EHO). It is inspired by the breeding process of the elk herd. Elks have two main breeding seasons: rutting and calving. In the rutting season, the elk herd splits into different families of various sizes. This division is based on fighting for dominance between bulls, where the stronger bull can form a family with large numbers of harems. In the calving season, each family breeds new calves from its bull and harems. This inspiration is set in an optimization context where the optimization loop consists of three operators: rutting season, calving season, and selection season. During the selection season, all families are merged, including bulls, harems, and calves. The fittest elk herd will be selected for use in the upcoming rutting and calving seasons. In simple words, EHO divides the population into a set of groups, each with one leader and several followers in the rutting season. The number of followers is determined based on the fitness value of its leader group. Each group will generate new solutions based on its leader and followers in the calving season. The members of all groups including leaders, followers, and new solutions are combined and the fittest population is selected in the selection season. The performance of EHO is assessed using 29 benchmark optimization problems utilized in the CEC-2017 special sessions on real-parameter optimization and four traditional real-world engineering design problems. The comparative results were conducted against ten well-established metaheuristic algorithms and showed that the proposed EHO yielded the best results for almost all the benchmark functions used. Statistical testing using Friedman’s test post-hocked by Holm’s test function confirms the superiority of the proposed EHO when compared to other methods. In a nutshell, EHO is an efficient nature-inspired swarm-based optimization algorithm that can be used to tackle several optimization problems.