Practical Engineering Applications Research Articles

Improved active vibration control of a cantilever beam using MFC actuators with Hammerstein model-based hysteresis modeling and VSS-FxLMS control algorithm

This study aims to enhance active vibration control in a cantilever beam using macro fiber composite (MFC) actuators. To address the challenge of accurately modeling the complex hysteresis behavior of MFC actuators, an advanced hysteresis modeling technique is employed, integrating a non-symmetric Bouc–Wen model with an Auto-Regressive model with eXogenous inputs (ARX). The parameter estimation of this model is optimized using an adaptive mutation factor, and multiple mutation strategy differential evolution (AmFMMDE) algorithm. Additionally, a Variable Step Size Filtered-x Least Mean Square (VSS-FxLMS) algorithm with step size adaptation based on normalized error change is utilized for effective vibration control. This approach shows promising potential for practical engineering applications requiring precise and efficient active vibration control.

An experimental investigation to predict the compressive strength of lightweight Ceramsite aggregate UHPC using boosting and bagging techniques

Ceramsite aggregate concrete is renowned for its lightweight properties but often exhibits low compressive strength (CS), limiting its structural applications. This study introduces ultra-high-performance powder into Ceramsite aggregate concrete to enhance its CS while maintaining low density. Achieving optimal CS for this composite material requires a carefully designed mix. In this research, advanced machine learning (ML) techniques, including random forest, extreme gradient boosting, categorical boosting, light gradient boosting machine (LightGBM), natural gradient boosting (NGBoost), and adaptive boosting, were employed to predict the CS of lightweight Ceramsite aggregate UHPC (LWCA-UHPC). The study utilized 189 datasets from the authors' own experimental results for training and testing the models. The results demonstrated that NGBoost and LightGBM outperformed other models, with R² values of 0.9614 and 0.9645, respectively, in the testing phase. The models also showed low errors, with NGBoost having a mean absolute error (MAE) of 2.0624 and a root mean squared error (RMSE) of 2.9140, while LightGBM recorded an MAE of 2.0161 and an RMSE of 2.7778. Moreover, feature importance and Shapley additive explanations (SHAP) analyses revealed that the most influential factors affecting the CS of LWCA-UHPC were cement content, density, sand, and Ceramsite aggregate content, consistent with experimental findings. Additionally, a graphical user interface was developed to integrate the hybrid ML model into a user-friendly desktop tool, achieving an R² value of 0.9671 with a low mean squared error of 3.5204 when tested against experimental datasets. This tool not only enables practical applications in engineering but also helps reduce the need for extensive experimental work by providing accurate predictions of CS.

Experimental study on the cyclic performance of innovative self-centering three-dimensional isolation bearing

To improve vertical seismic isolation and seismic resilience in isolation bearings, this paper introduces an innovative three-dimensional isolation bearing combining a disc-spring system (DSS), a high-damping rubber bearing (HDR), and a horizontal spring system (HSS). The DSS is designed with low vertical stiffness to enhance vertical isolation, while the HSS provides self-centering force to improve the seismic resilience of the HDR. This study first presents a comprehensive theoretical analysis of the horizontal and vertical stiffness composition. The vertical performance of this three-dimensional isolation bearing is attributed to the DSS, while the HSS and HDR jointly contribute to its horizontal performance. Seven specimens were designed and subjected to shear-compression cyclic tests to assess the horizontal and vertical cyclic performance. Experimental parameters included loading amplitude, loading frequency, vertical pre-load, and DSS combinations for vertical tests, and axial compression force, loading frequency, shear strain, and HSS function in horizontal tests. The cyclic performance parameters for the three-dimensional isolation bearing, including equivalent damping ratio, equivalent stiffness, and energy dissipation, was evaluated. In general, the proposed three-dimensional isolation bearing exhibits stable cyclic performance and advantageous seismic resilience, warranting its promotion to practical engineering applications.

Inversion Method for Material Parameters of Concrete Dams Using Intelligent Algorithm-Based Displacement Separation

Integrating long-term observational data analysis with numerical simulations of dam operations provides an effective approach to dam safety evaluation. However, analytical results are often subject to errors due to challenges in accurately surveying and modeling the foundation, as well as temporal changes in foundation properties. This paper proposes a concrete dam displacement separation model that distinguishes between deformation caused by foundation restraint and that induced by external loads. By combining this model with intelligent optimization techniques and long-term observational data, we can identify the actual mechanical parameters of the dam and conduct structural health assessments. The proposed model accommodates multiple degrees of freedom and is applicable to both two- and three-dimensional dam modeling. Consequently, it is well-suited for parameter identification and health diagnosis of concrete gravity and arch dams with extensive observational data. The efficacy of this diagnostic model has been validated through computational case studies and practical engineering applications.

Plastic Constitutive Training Method for Steel Based on a Recurrent Neural Network

The deep learning steel plastic constitutive model training method was studied based on the recurrent neural network (RNN) model to improve the allocative efficiency of the deep learning steel plastic constitutive model and promote its application in practical engineering. Two linear hardening constitutive datasets of steel were constructed using the Gaussian stochastic process. The RNN, long short-term memory (LSTM), and gated recurrent unit (GRU) were used as models for training. The effects of the data pre-processing method, neural network structure, and training method on the model training were analyzed. The prediction ability of the model for different scale series and the corresponding data demand were evaluated. The results show that LSTM and the GRU are more suitable for stress–strain prediction. The marginal effect of the stacked neural network depth and number gradually decreases, and the hysteresis curve can be accurately predicted by a two-layer RNN. The optimal structure of the two models is A50-100 and B150-150. The prediction accuracy of the models increased with the decrease in batch size and the increase in training batch, and the training time also increased significantly. The decay learning rate method could balance the prediction accuracy and training time, and the optimal initial learning rate, batch size, and training batch were 0.001, 60, and 100, respectively. The deep learning plastic constitutive model based on the optimal parameters can accurately predict the hysteresis curve of steel, and the prediction abilities of the GRU are 6.13, 6.7, and 3.3 times those of LSTM in short, medium, and long sequences, respectively.

An anti-erosion cylindrical surface incorporating two bionic elements

Abstract Erosive is an inevitable and persistent form of wear, which predominantly occurs on curved surfaces within the realm of fluid machinery. To address this issue, we have developed a novel model incorporating two bionic elements, namely bionic arrangement and bionic morphology, and applied it to explore the erosion resistance of cylindrical surfaces. Specifically, the bionic arrangement is inspired by the phyllotaxis arrangement observed in plants, while the bionic morphology involves the incorporation of convex unit morphology found in desert organisms. Employing a comprehensive approach encompassing erosion testing and numerical analysis, we established two comparative test groups that differed in terms of arrangement and distribution density. This comprehensive analysis sheds light on the erosion resistance mechanism inherent in the combined bionic model. The findings of this study hold significant theoretical implications for the advancement of bionic anti-erosion technology and its practical applications in engineering.

On the inverse problem of parameter identification in the steady Oseen model with unilateral and frictional‐type boundary conditions

AbstractRecently, Migórski‐Dudek investigated a steady Oseen flow for a generalized Newtonian incompressible fluid with unilateral and frictional‐type boundary conditions. They established an existence theorem for the steady Oseen model when the divergence‐free convection field and acting volume force are known. However, prior knowledge of and is often impossible in practical engineering applications. This leads to the question of whether the divergence‐free convection field and acting volume force can be determined. The main objective of this paper is to provide a positive response to this challenging and intriguing question. Specifically, we aim to formulate an inverse problem for identifying and , characterized by a nonlinear and nonsmooth regularized optimization problem. Initially, we introduce a variational selection mapping for the steady Oseen model. We then establish the boundedness and generalized continuity of this variational selection. Finally, by leveraging optimization theory, convex analysis, and nonsmooth analysis, we develop an abstract regularization framework for the considered inverse problem and establish the existence of a solution.

Experimental Implementation of Reinforcement Learning Applied to Maximise Energy from a Wave Energy Converter

Wave energy has the potential to provide a sustainable solution for global energy demands, particularly in coastal regions. This study explores the use of reinforcement learning (RL), specifically the Q-learning algorithm, to optimise the energy extraction capabilities of a wave energy converter (WEC) using a single-body point absorber with resistive control. Experimental validation demonstrated that Q-learning effectively optimises the power take-off (PTO) damping coefficient, leading to an energy output that closely aligns with theoretical predictions. The stability observed after approximately 40 episodes highlights the capability of Q-learning for real-time optimisation, even under irregular wave conditions. The results also showed an improvement in efficiency of 12% for the theoretical case and 11.3% for the experimental case from the initial to the optimised state, underscoring the effectiveness of the RL strategy. The simplicity of the resistive control strategy makes it a viable solution for practical engineering applications, reducing the complexity and cost of deployment. This study provides a significant step towards bridging the gap between the theoretical modelling and experimental implementation of RL-based WEC systems, contributing to the advancement of sustainable ocean energy technologies.

Development of sustainable self-compacting concrete: Replacement of sand with municipal solid waste incineration ash molten slag and recycled fine aggregate

The purpose of this study is to develop self-compacting concrete using MSWA and RA as fine aggregates, supplemented with fly ash and GGBS that can modify the workability of the concrete. To evaluate the performance of sustainable self-compacting concrete, its mechanical properties, workability, drying shrinkage, and pore structure were comprehensively analyzed. The results demonstrate the feasibility of producing self-compacting concrete by replacing 50–100 % of sand with MS and RA, as all mixes in this experiment achieved the target slump flow of 600 ± 20 mm. The compressive strength remained unaffected for MS contents below 25 %, but at 50 % MS, it was 10 % lower than that of normal concrete. The drying shrinkage deformation of all specimens was below 700 μ, meeting the requirements for practical engineering applications. Incorporating fly ash increases porosity but transforms micropores larger than 100 nm into those smaller than 100 nm. Incorporating GGBS not only reduces porosity but also converts pores of 10–100 nm into gel pores smaller than 10 nm. Additionally, the suitability of various formulas correlating compressive strength and static elasticity modulus, recommended by American Concrete Institute (ACI 363), European Practice (EN 1992), and the Architectural Institute of Japan (JASS 5), was evaluated, and appropriate formulas were determined through regression analysis.

Exploring Emergent Nanotechnology Applications in Mechanical and Health Engineering

Abstract: Nanotechnology is a promising area of research in science and technology with potential benefits in automotive, aircraft, construction, energy, healthcare, agriculture, food processing, although it has its own risks in environmental sustainability. Recent advances in this area have stimulated research and their applications in different fields, with important results in mechanical engineering and health engineering. The aim of this article is to give an overview of some recent advances of nanotechnology, exploring emerging applications of these typical technologies. Examples of some successful practical applications in mechanical and healthcare engineering based on this technology are given, and an analysis of the prospects and main problems of this area is also conducted. Furthermore, mechanical engineers and health professionals would find this review valuable in updating their knowledge on the latest developments in nanotechnology applications.

Estimation of compressive strength of concrete with manufactured sand and natural sand using interpretable artificial intelligence

To satisfy the design strength of manufactured sand concrete (MSC) in practical engineering applications, a plethora of geotechnical tests are frequently conducted. An effective approach is imperative to reduce the consumption of labor and resources during these tests. The objective of this paper is to introduce an interpretable machine learning (ML) method to evaluate the compressive strength (CS) of MSC. Firstly, a dataset was established by compiling experimental results from 208 literatures. 3382 data points were selected from the dataset for algorithm training. Recursive Feature Elimination with Cross-Validation (RFECV) was employed to select input parameters. Four algorithms with 12 selected input variables and 1 output variable were constructed to predict CS of MSC using Random Forest (RF), Gradient Boosting Decision Trees (GBDT), eXtreme Gradient Boosting algorithm (XGBoost), and Categorical Boosting (CatBoost). The results show that XGBoost has the highest accuracy and generalization ability (R2=0.934, MAE=3.44, RMSE=5.16, MAPE=0.07). To enhance model transparency, SHapley Additive exPlanations (SHAP) was adopted to explain the underlying predictive mechanisms of ML models. Analyses show that, 1) the cement content, curing time, and water content were the main feature parameters influencing the CS of MSC, 2) the cement content, curing time, and water content have a linear increase, logarithmic increase, and exponential decrease with the CS of MSC, respectively. Partial Dependence Plots (PDP) and Individual Conditional Expectation (ICE) plots were used to further analyze the impacts of these significant influencing factors on the CS of MSC. Additionally, the Local Interpretable Model-Agnostic Explanations (LIME) method was employed to investigate thresholds for various material dosages in MSC containing 5–10 % stone powder. Two typical scenarios were selected for analysis, yielding recommended dosage ranges for concrete of two distinct strengths. Finally, a graphical user interface (GUI) for the CS of MSC has been designed, which might be of great use to material engineers. This provides reference and guidance for concrete engineering practice.

Dynamic Modeling and Analysis of Multi-Span Timoshenko Beams Under Moving Loads

Dynamic response analysis of multi-span beams is one of the important topics in the field of highway and railway engineering, which provides valuable guidance for the design, operation and maintenance of multi-span bridges. It is worth considering how to calculate the dynamic characteristics of multi-span beams quickly and conveniently. In this study, an effective analytical method is developed for the dynamic modeling and investigations of multi-span Timoshenko beams under moving loads by employing the assumed mode method (AMM). Based on Hamilton’s principle, the equation of motion of the Timoshenko multi-span beam subjected to moving loads is formulated and the natural frequencies are calculated. A comprehensive investigation is conducted on the dynamic responses of the multi-span Timoshenko beam considering different factors, including the length-to-thickness ratio, disorder degree, arrangement order, number of spans and related parameters of moving loads. In addition, the influence of the interval and velocity of the moving load series on the dynamic responses, including displacement, velocity and acceleration of the multi-span beam, are analyzed in detail. The theoretical calculation results of multi-span Timoshenko beams under moving loads are compared with numerical results obtained from ANSYS software, and the results are in good agreement. This demonstrates that the developed method, which utilizes the AMM to analyze the dynamic characteristics of multi-span beams subjected to moving loads, significantly simplifies the calculations and is well-suited for practical engineering applications.

Review of Cathodic Protection Technology for Steel Rebars in Concrete Structures in Marine Environments

This study compared the advantages and disadvantages of various corrosion protection methods for steel rebars and clarified the advantages of the cathodic protection (CP) method in the application of corrosion protection in marine structures. The advantages and disadvantages of sacrificial anodes and impressed current technology for the CP of steel rebars in marine structures were further discussed in detail, and the feasibility of CP applications in practical engineering was evaluated.

Impact and freezing characteristics of deionized water droplets on cold curved surfaces

Processes involving droplet impact and subsequent freezing occur widely in practical engineering applications. In the present study, a visualization experimental setup is utilized to investigate the effects of the impact of single millimeter-scale droplets on curved surfaces at room and low temperatures. The influences of the Weber number We, wall temperature, and wall wettability on the dynamics of droplet impact and the characteristics of ice formation are examined. The morphological evolution of droplet impact and the variations of the dimensionless spreading coefficient are analyzed. The results indicate that at high We (We = 277), droplets reach their maximum spread on cold walls in a shorter time than on room-temperature walls, and their peak spreading coefficient is smaller. Upon impact with a cold wall, droplets exhibit a spread–splatter behavior. Low temperatures suppress the oscillatory behavior of droplets on a curved wall. In the case of a hydrophilic wall surface, as the impact We increases from 42 to 277, the impact mode gradually transitions from spread–retract–freeze to spread–splatter–freeze. The maximum spreading coefficient first increases and then decreases with increasing impact We. At high We (We = 277), the wall wettability has a minimal effect on the dynamics of droplet impact and freezing, with a spread–splatter–freeze mode being exhibited for both hydrophobic and hydrophilic walls, and the final freezing morphology is similar.

Full-field displacement measurements of structural vibrations using a novel two-stage neural network

This paper presents a novel two-stage neural network for quantifying full-field displacements of vibrating structures. A graphics software was used to simulate structural vibrations with random displacement fields and generate image datasets through batch rendering. Subsequently, a two-stage neural network model comprising an object segmentation subnetwork for extracting structural shapes and an optical-flow subnetwork for identifying displacement fields was constructed. A coordinate convolution module was embedded in the optical-flow estimation model and the two subnetworks were integrated to develop a comprehensive model. An experiment was designed to monitor the vibration displacements in a cantilever column. The results showed that the proposed model achieved optimal recognition accuracy with a mean absolute error of 0.1477 pixels and root-mean-squared error of 0.2335 pixels. Additionally, it exhibited robustness to various lighting conditions. Furthermore, case studies on real-world structural vibration videos substantiated the practical engineering application value of the proposed model.

Effects of liquid accelerators on long-term mechanical strength development and microstructural changes of wet-mixed shotcrete

The significant long-term strength loss of shotcrete can greatly affect the safety and stability of underground shotcrete structures. Developing high-quality liquid accelerators remains an indispensable research direction and a viable approach to mitigate challenge. This study synthesizes three distinct kinds of liquid accelerators - alkali-free fluorine-free (GJ), alkali-free fluorine-containing (MJ), and low-alkaline (JJ) and investigates their effects on the long-term mechanical strength of wet-mixed shotcrete. Moreover, changes in the phase composition and microstructure of accelerated shotcrete were explored utilizing X-ray diffraction, thermogravimetric analysis, mercury intrusion porosimetry, and scanning electron microscopy techniques to reveal underlying mechanisms. Results indicate that accelerated cement paste and mortar with a 6 wt% fixed dosage of liquid accelerators exceeded the prescribed basic performance indices (setting time and 28-day compressive strength ratio) for shotcrete applications. Among the accelerators, the alkali-free fluorine-containing type exhibited the most beneficial effect on long-term strength, attributed to increased C-(A)-S-H formation from added Al and Si into the system. Morphological and crystal composition changes in hydrate phases, notably the transformation of ettringite to AFm and additional formation of monosulfate and hemicarbonate, increased the solid volume, refined pore structure and reduced total porosity, thus enhancing long-term strength. This study offers valuable insights into the synthesis and optimization of main accelerator components to mitigate long-term strength loss in shotcrete for practical engineering applications.

BHT-QAOA: The Generalization of Quantum Approximate Optimization Algorithm to Solve Arbitrary Boolean Problems as Hamiltonians.

A new methodology is introduced to solve classical Boolean problems as Hamiltonians, using the quantum approximate optimization algorithm (QAOA). This methodology is termed the "Boolean-Hamiltonians Transform for QAOA" (BHT-QAOA). Because a great deal of research and studies are mainly focused on solving combinatorial optimization problems using QAOA, the BHT-QAOA adds an additional capability to QAOA to find all optimized approximated solutions for Boolean problems, by transforming such problems from Boolean oracles (in different structures) into Phase oracles, and then into the Hamiltonians of QAOA. From such a transformation, we noticed that the total utilized numbers of qubits and quantum gates are dramatically minimized for the generated Hamiltonians of QAOA. In this article, arbitrary Boolean problems are examined by successfully solving them with our BHT-QAOA, using different structures based on various logic synthesis methods, an IBM quantum computer, and a classical optimization minimizer. Accordingly, the BHT-QAOA will provide broad opportunities to solve many classical Boolean-based problems as Hamiltonians, for the practical engineering applications of several algorithms, digital synthesizers, robotics, and machine learning, just to name a few, in the hybrid classical-quantum domain.

Pathways and enhancement strategies for magnesium hardness removal in modified induced crystallization softening

Elevated levels of total hardness in drinking water can readily result in scaling, which poses a threat to both the safety of water quality and the convenience of its use. While there is a wealth of research on the removal of calcium hardness, there is a dearth of studies focusing on the removal of magnesium hardness. In light of this, the present study employs modified induced crystallization softening (MICS) to delineate the removal pathways and mechanisms of magnesium hardness, and to investigate viable methods for its enhancement and application. Our research has determined that magnesium hardness can be effectively removed from water through the MICS, with the dosage of softening agents (NaOH) being a significant factor that influences this removal, whereas the fixed bed height within the fluidized bed exerts minimal impact on the process. In the low-dose stage (less than 250 mg/L), when the pH is below 10.0, up to 20% of magnesium hardness can be removed, predominantly through the crystallization of (Ca0.936Mg0.064)CO3. As the dosage increases to the moderate stage (250–400 mg/L), the conversion of excess bicarbonate (HCO3−) to carbonate (CO32−) in the water hinders further removal of magnesium hardness. In the high-dose stage (exceeding 400 mg/L), when the pH rises above 10.5, the removal rate of magnesium hardness can be enhanced to over 75%, with the crystallization of Mg(OH)2 being the primary removal mechanism. Density functional theory calculations, along with molecular dynamics simulations of cohesive energy and bond energy, substantiate the feasibility of the identified magnesium removal pathways. The addition of coagulants (FeCl3) and an decrease in the up-flow velocity can further augment the removal efficiency of magnesium hardness by promoting the crystallinity of Mg(OH)2 during the high-dose stage (exceeding 400 mg/L). In practical engineering applications, the strategic control of softening agent dosages enables the achievement of varying levels of magnesium hardness removal, tailored to specific water quality requirements.

Intelligent controllable ultrafast fiber laser via deep learning and adaptive optimization algorithm

Ultrafast fiber lasers based on nonlinear polarization rotation can generate femtosecond pulses with different pulse durations and high peak powers, which are powerful tools for engineering applications and scientific research. However, achieving a precise and repeatable polarization state for generating the ultrashort pulses with the shortest pulse duration remains a significant challenge. In this paper, we extend the use of recurrent neural networks and adaptive optimization algorithms, specifically designed to optimize repetitive processes in optical systems, to facilitate intelligent search and control aimed at achieving the minimum pulse duration within a mode-locked fiber laser cavity. Our multi-algorithm-based intelligent system can fully simulate and optimize the processes involved in hands-on experiments. Our intelligent system identified a mode-locked fiber laser with the shortest pulse duration of 465 fs, which was experimentally verified. The proposed intelligent algorithm not only identifies the shortest pulse but also holds significant potential for selecting related laser characteristic parameters. We believe this work opens up a novel avenue for exploration and optimization in mode-locked lasers and the intelligent laser can find practical applications in engineering and scientific research.

Q-Learning-Based Dumbo Octopus Algorithm for Parameter Tuning of Fractional-Order PID Controller for AVR Systems

The tuning of fractional-order proportional-integral-derivative (FOPID) controllers for automatic voltage regulator (AVR) systems presents a complex challenge, necessitating the solution of real-order integral and differential equations. This study introduces the Dumbo Octopus Algorithm (DOA), a novel metaheuristic inspired by machine learning with animal behaviors, as an innovative approach to address this issue. For the first time, the DOA is invented and employed to optimize FOPID parameters, and its performance is rigorously evaluated against 11 existing metaheuristic algorithms using 23 classical benchmark functions and CEC2019 test sets. The evaluation includes a comprehensive quantitative analysis and qualitative analysis. Statistical significance was assessed using the Friedman’s test, highlighting the superior performance of the DOA compared to competing algorithms. To further validate its effectiveness, the DOA was applied to the FOPID parameter tuning of an AVR system, demonstrating exceptional performance in practical engineering applications. The results indicate that the DOA outperforms other algorithms in terms of convergence accuracy, robustness, and practical problem-solving capability. This establishes the DOA as a superior and promising solution for complex optimization problems, offering significant advancements in the tuning of FOPID for AVR systems.