Search Results (608)

Plate–fin heat exchangers (PFHEs) are widely used in petrochemical, energy and electric power, aerospace, and other industries with large heat transfer requirements. The development of performance prediction and optimization methods for PFHEs has become increasingly important in the design and operation of such heat exchangers (HEs). This paper establishes a database of flow and heat transfer characteristics for four types of PFHEs with different structural parameters. Based on this database, the back-propagation artificial neural network (BP-ANN) model was optimized using the particle swarm optimization (PSO) algorithm to form the PSO-BP-ANN model for the performance prediction of these four types of PFHEs. This combination has been found to improve the prediction accuracy and generalization ability of the BP-ANN model. Additionally, the non-dominated sorting genetic algorithm II (NSGA-II) method was used to characterize the relationship between four structural parameters to be optimized (the length, height, spacing, and thickness of the HE fin) and the two objective functions (j and f) of the serrated PFHE in laminar flow. This enables the Pareto optimal solution to be obtained. The results show that, under laminar flow conditions (Re = 800), the serrated fin HE achieves the best heat transfer performance when the fin height, spacing, thickness, and length are 9.29, 1.22, 0.16, and 3.06, respectively. Full article

Accurate gear fault diagnosis under variable operating conditions remains challenging because vibration signals are affected by noise, speed-load variations, and condition-dependent feature shifts. To address these issues, this study proposes a gear fault diagnosis framework that integrates multidomain vibration feature fusion with a back-propagation neural network optimized by the sparrow search algorithm (SSA-BP). Vibration signals collected from a planetary gearbox fault-implantation platform were used to identify seven health states, including normal condition, sun gear pitting, sun gear fracture, sun gear wear, planetary gear pitting, planetary gear fracture, and planetary gear wear. For each signal segment, a 20-dimensional feature vector was constructed by combining nine time-domain features, three frequency-domain features, and eight wavelet packet energy features. SSA was employed to optimize the initial weights and biases of a double-hidden-layer BP neural network before supervised training. Experimental results show that the proposed feature fusion scheme achieved a classification accuracy of 98.30%, outperforming single-domain and pairwise feature combinations. In overall fault classification, SSA-BP obtained 98.26% accuracy, 98.26% macro-recall, 98.27% macro-precision, and 98.26% macro-F1. Moreover, SSA-BP reduced the convergence iterations from 826 to 312 compared with traditional BP and maintained 95.18% accuracy under high-speed and high-load conditions with scarce training samples. These results demonstrate that the proposed SSA-BP model provides improved convergence efficiency, diagnostic accuracy, and cross-condition robustness for intelligent gearbox condition monitoring. Full article

Rail-transit station areas concentrate commercial services, employment, and intensive land development, but their vitality is shaped by multiple built-environment conditions rather than rail accessibility alone. Focusing on 20 stations along the Osaka Metro Midosuji Line in Japan, this study uses Japanese chome units, which are small neighborhood-level address and statistical units, within an 800 m pedestrian catchment as analytical units and measures commercial-service agglomeration intensity, employment intensity, and commercial–employment composite vitality. The composite indicator measures the static co-concentration of commercial-service provision and employment carrying capacity, with pedestrian flow, consumption activity, and dwell time treated as separate dimensions of station-area vitality. Ten station-area environmental variables are examined using ordinary least squares (OLS), Lasso, Random Forest, Back-Propagation (BP) Neural Network, and extreme gradient boosting (XGBoost) models, with Shapley additive explanations (SHAP) applied to interpret variable contributions and nonlinear responses. Results show that nonlinear models generally outperform linear models. Development intensity, officially assessed land price, and network distance to the nearest metro station are the most influential variables, showing threshold, marginal, and non-monotonic effects. Split models indicate that commercial-service agglomeration is more sensitive to rail proximity and street-network conditions, whereas employment intensity is more associated with development intensity and land price. These findings support fine-grained station-area renewal and mixed-function planning. Full article

With the advancement of China’s carbon peaking and carbon neutrality targets and the low-carbon upgrading of the construction industry, steel fiber recycled aggregate concrete (SFRAC) has attracted increasing attention as a sustainable construction material due to its advantages in resource recycling and enhanced mechanical performance. However, its compressive strength is influenced by multiple interacting factors, making accurate prediction challenging when using conventional empirical or regression-based methods. To enhance predictive performance, a compressive strength database was established based on published experimental data. The input layer included seven mixture parameters: water content, cement content, fine aggregate content, natural coarse aggregate content, recycled coarse aggregate content, steel fiber content, and superplasticizer dosage, with the 28-day compressive strength serving as the output variable. Using this database, four prediction models were developed, including a back-propagation (BP) neural network and three optimized variants—GA–BP, PSO–BP, and GA–PSO–BP, optimized by genetic algorithm (GA) and particle swarm optimization (PSO)—were developed. Their performance was evaluated using the coefficient of determination (R²), root mean square error (RMSE), and mean absolute error (MAE). Among the four models, GA–PSO–BP produced the best predictive performance, with a best-run R² of 0.9308 on the validation set, exceeding the BP, GA–BP, and PSO–BP neural networks by 0.0642, 0.0326, and 0.0512, respectively. Over 10 independent runs, it attained an average R² of 0.8822 and consistently delivered the lowest RMSE and MAE with small standard deviations, confirming its superior predictive accuracy and stability. These findings suggest that integrating GA and PSO can effectively enhance the predictive accuracy and stability of the BP neural network, thereby providing a dependable reference for compressive strength prediction and mix proportion optimization of steel fiber recycled aggregate concrete. Full article

The turbine-driven coaxial boiler feed pump (TD-BFP) speed regulation system is a core auxiliary machine in thermal power generating units. Its complex physical characteristics, including strong square-law nonlinearity, multivariable coupling, and large inertia, pose significant challenges for conventional fixed-parameter PID controllers, which often suffer from severe regulation lag, integral windup, and high-frequency oscillation during wide-range operating condition transitions. To address these issues, an improved adaptive PID control strategy based on a Back Propagation (BP) neural network is proposed in this paper. Specifically, to overcome the negative control gradient loss caused by the square-law resistance in the physical model, a sign-preserving mapping logic ($u\mid u\mid$) is innovatively designed. Furthermore, a dynamic anti-integral windup mechanism with physical boundary constraints and a first-order inertial filtering algorithm is introduced. Comprehensive simulation experiments on the Matlab/Simulink platform under high-load step operating conditions (3683 r/min and 1104 t/h) reveal that the proposed algorithm achieves millisecond-level, zero-overshoot tracking. Quantitative evaluations demonstrate that, compared with the traditional PID controller, the proposed method reduces the Root Mean Square Error (RMSE) by 88.29% and the Integral of Absolute Error (IAE) by 93.75%, achieving a near-perfect goodness of fit ($R^2$) of 0.9998. Additionally, the Total Variation (TV) of the control command is substantially decreased. These results convincingly demonstrate that the proposed controller perfectly balances extremely high dynamic fitting accuracy with reduced mechanical wear, presenting exceptional engineering application value for the localization transformation of power plant control systems. Full article

To enhance the accuracy, stability, and interpretability of residential building cost prediction models, and thereby provide a reliable basis for project investment decision-making. This study takes Sichuan Province as the research area and develops an improved sparrow search algorithm (ISSA). The performance of the Genetic Algorithm (GA), Wolf Pack Algorithm (WPA), Sparrow Search Algorithm (SSA), and ISSA was first evaluated and compared using benchmark test functions. Subsequently, nine prediction models, including Back Propagation Neural Network (BP), GA-BP, WPA-BP, SSA-BP, ISSA-BP, Random Forest (RF), ISSA-RF, Extreme Gradient Boosting (XGBoost), and ISSA-XGBoost, were established for comparative analysis. Finally, SHapley Additive exPlanations (SHAP) were employed to rank the key factors affecting construction cost. The results show that: (1) The ISSA algorithm demonstrates excellent convergence accuracy, stability and speed on benchmark test functions. (2) The ISSA-BP model achieved an average coefficient of determination (R²) of 0.9773, an average root mean square error (RMSE) of 39.2339, an average mean absolute error (MAE) of 17.0973, an average mean absolute percentage error (MAPE) of 0.6293, and an average mean bias error (MBE) of 9.1583. Compared with the other models, ISSA-BP exhibited the best overall predictive performance. (3) SHAP analysis indicates that indicators such as total building area and structure type have the greatest impact on project cost, while roof form and roof waterproofing have the least influence. This study can serve as a reference for refining and intelligently managing construction project costs. Full article

In this article, a data-driven adaptive neural network additional damping controller (DDANN-ADC) is proposed to regulate rotor-side converters of a DFIG-based power system to suppress sub-synchronous control interaction (SSCI). Firstly, a back propagation (BP) intermediate variable observer is designed to construct a dynamic model of DFIG-based wind farms based on real-time input–output measurement data. Subsequently, a modified cost function is developed for a BP online controller to generate a target control law, thereby contributing additional damping to the DFIG-based power system. The proposed DDANN-ADC can effectively utilize limited data generated during the control process to achieve online system identification and precise control of the system. Then, the stability of DFIG-based power system under the proposed DDANN-ADC is demonstrated with the Lyapunov function. Finally, simulation results reveal that the proposed DDANN-ADC methodology outperforms the traditional method with better adaptability and robustness under different operational conditions. Full article

The integration of digital technologies is increasingly recognized as a critical enabler of lean practices in prefabricated construction projects. However, a systematic understanding of the underlying factors that drive this lean–digital transformation remains limited. To address the gap, this study identified 18 factors through an in-depth review of 30 papers and a follow-up questionnaire survey. The factors are divided into five dimensions, i.e., organizational, social, technological, economic and environmental, according to an extended framework of the Socio-Technical Systems (STS) and Technology–Organization–Environment (TOE). These 18 factors were then analyzed via a back propagation (BP) neural network model. The empirical data were collected from 148 practitioners across 11 regions in China where PC industrialization, digital technology adoption, and lean-related practices are relatively mature. These regions were selected because digitalization-driven lean practices are more observable in such contexts, allowing the BP model to capture the comprehensive contribution of key factors more effectively. The findings reveal that the effective implementation of the smart lean practices via digitalization is primarily driven by a systematic process, where greater attention should be directed toward simulation-based process optimization, robust information management, integrated design and construction, lean management systems, and the workers’ digital skills. Although the empirical evidence is derived from relatively mature PC and digital construction markets in China, the identified factors provide reference insights for broader PC projects including less mature regions to make effective measures to improve lean implementation. This study contributes to the existing knowledge body of lean in PC by extending the theories of STS and TOE to advance the understanding of digital drivers. Additionally, the results serve as a reference for stakeholders by informing strategic priorities such as resource allocation for workforce development, advancing the realization of smart lean prefabricated construction. Full article

In the domain of river engineering, estimating the total sediment load in rivers is a crucial challenge. For tens to hundreds of kilometers downstream, the additional sand and gravel in the sediment can raise the elevation of channel beds. For highly braided rivers like the Brahmaputra-Jamuna, the accurate prediction of the total sediment load depends on the complex relationships among different hydro-meteorological variables. As a result, manual selection of the lagged features from only antecedent sediment records can produce suboptimal predictions, which can be considered a significant research gap. In addition, the predictive accuracy can be further enhanced through the application of advanced decomposition techniques. To address these deficiencies, we implemented three sophisticated feature selection methodologies: SelectKBest, Mutual Information, and Random Forest utilizing the Boruta Algorithm as an alternative to manual feature selection. Furthermore, we investigated complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), variational mode decomposition (VMD), and the Hodrick–Prescott Filter (HPF) to improve data mining efficiency. Four distinct artificial neural network (ANN) training algorithms were considered: back propagation (BP), cascade correlation (CC), conjugate gradient (CG), and Levenberg–Marquardt (LM), as alternatives to the conventional BP-based training approach. The effectiveness of the variants of the ANN was assessed in comparison to a powerful ensemble learning model, specifically the decision tree (DT). Results indicate that the HPF-enhanced ANN-LM model exhibited the strongest performance metrics when compared to alternative techniques, with values of NRMSE = 0.004, MAE = 455.242 kg/s, NSE = 0.998, and KGE = 0.990. The outcomes from Sobol’s sensitivity analysis suggest that the sediment dynamics in this region can be better predicted through the inclusion of rainfall-based features. Full article

To overcome the challenges of processing soft-brittle potassium dihydrogen phosphate (KDP) crystals, this study proposes a back-propagation (BP) neural network model for the rapid prediction of the ion beam removal function using Faraday cup scanning data (a method that measures the spatial distribution of ion beam current density). By correlating current density measurements with point etching experiment results, the model accurately maps both the linear relationship (R² = 0.98) between peak removal rate and peak current density, and the non-linear relationship between the full width at half maximum (FWHM) of the beam and the removal function. The predicted removal function demonstrates high accuracy, with a volume removal rate error of just 2.56% compared to experimental results. Furthermore, this method drastically reduces calculation time from approximately 2 h (required by the conventional point-etching experiment, which involves iterative vacuum cycling, etching, and ex situ interferometry) to just 2 min, significantly improving efficiency. Applied to the ion beam polishing of a 50 mm × 50 mm × 10 mm KDP crystal, the model proved highly effective. The surface figure error was corrected from an initial 0.298λ peak-to-valley (PV) and 0.0496λ root-mean-square (RMS) to 0.167λ PV and 0.036λ RMS, where λ (632.8 nm) is the wavelength of the He-Ne laser used for interferometric surface measurement, achieving a convergence ratio (defined as the ratio of initial PV to final PV) of 1.78. This research provides a high-efficiency, high-precision technical solution for manufacturing KDP components for inertial confinement fusion (ICF) applications. Full article

This paper focuses on the application of ultrasonic guided waves in the defect inspection of buried pipelines, selecting circumferential cracks as the research object. Based on the three-dimensional finite element pipeline model, numerical simulation methods are employed to obtain defect echo signals for investigating the circumferential positioning accuracy, and the quantitative identification of the circumferential length and depth of crack geometries. For the circumferential positioning, a new circumferential positioning method is proposed. By introducing the circumferential position coefficient, the central angle is determined between the defect center point and the node corresponding to the maximum peak-to-peak amplitude of the echo signal. The maximum positioning error reaches 0.89% of the angle of the entire circle, achieving precise circumferential positioning of defects, and providing technical support for the development of future defect detection devices that use circumferential positioning. Regarding the identification of defect geometries, a back propagation (BP) neural network model is built for realizing the inversion of defect geometries, which is trained by using 13 feature indicators of numerical simulation datasets of defect echo signals in the time, frequency, and time–frequency domains. The trained model is then used to predict both the circumferential length and depth of cracks excluded from the dataset. The results demonstrate a maximum error of 0.46% of the pipe circumference for length and of 4% of the pipe wall thickness for depth. This high-precision inversion of the circumferential length and depth of cracks demonstrates that the model can significantly improve the detection accuracy of defect geometries in engineering applications. Full article

To fully explore the short-term fluctuation characteristics of water quality monitoring data and improve the accuracy of water quality prediction models, this study proposes a hybrid water quality prediction model based on the Runge–Kutta optimization algorithm, ensemble empirical mode decomposition (EEMD), Temporal Convolutional Network (TCN), and Bidirectional Long Short-Term Memory (BiLSTM) network. The optimized EEMD-TCN-BiLSTM model was applied to predict the permanganate index at the Sandao Section, and its prediction performance was compared with five mainstream models widely used in environmental science research, namely Bidirectional Long Short-Term Memory (BiLSTM) network, Back Propagation (BP) neural network, Long Short-Term Memory (LSTM) network, extreme gradient boosting (XGBoost), and Temporal Convolutional Network (TCN). The comparison results show that the proposed model can extract the characteristic information of short-term fluctuations in water quality data more effectively and significantly improve the accuracy of water quality prediction. The Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and coefficient of determination (R²) of the model reach 0.08288, 0.13152, and 0.95084, respectively, indicating reduced error indices and significantly improved fitting performance. The proposed model has superior prediction performance, higher prediction accuracy, and stronger generalization ability, which can provide scientific and quantitative technical support for real-time water quality monitoring, pollution risk early warning, and refined water environment management. Meanwhile, this model offers an integrated scientific approach for the sustainable development and utilization of water resources, and provides technical support for addressing water pollution and environmental sanitation, one of the core global sustainable development challenges. Full article

Soil respiration (Rs) is influenced by various factors, including soil temperature (ST), soil moisture (SM), and vegetation growth. Accurately and quantitatively estimating Rs from remote sensing data is essential for understanding the carbon cycle in desertification ecosystems. However, selecting appropriate vegetation representation factors poses a significant challenge during the remote sensing inversion. Sun-Induced Chlorophyll Fluorescence (SIF) is used extensively to monitor vegetation diseases and pests, assess drought conditions, and estimate Gross Primary Production (GPP). Nevertheless, the applicability of SIF for estimating Rs from remote sensing data and whether Rs modeling surpasses traditional vegetation indices requires further investigation. This study focuses on the Hongshaquan mining area, utilizing UAV hyperspectral, thermal infrared, and in situ monitoring data, combined with four machine learning methods: Random Forest (RF), Partial Least Squares (PLS), Support Vector Machine (SVM), and Back Propagation Neural Network Algorithm (BP) to establish a model for estimating Rs from remote sensing data. The determination coefficient (R²) and root mean square error (RMSE) were used to assess the performance of Rs inversion models characterized by SIF, Normalized Difference Vegetation Index (NDVI), and Near-Infrared Reflectance of Vegetation (NIRv) improved by radiance. The feasibility and modeling potential of estimating Rs from remote sensing data using SIF were explored. The results indicate that vegetation significantly impacts Rs in desertification mining area ecosystems, and the inversion accuracy of Rs improved by 26.8% after incorporating vegetation factors. The RF model displayed the best overall performance among the four machine learning methods. When the Salinity Index (SI) and Temperature Vegetation Dryness Index (TVDI) were treated as fixed components of the modeling independent variable, the modeling accuracy of the various vegetation representation factors ranked from highest to lowest as follows: SIF > NIRv > NDVI, with corresponding R² values of 0.63, 0.58, and 0.57, and RMSEs of 0.08 μmol·m⁻²·s⁻¹, 0.12 μmol·m⁻²·s⁻¹, and 0.13 μmol·m⁻²·s⁻¹, respectively. The research findings suggest that SIF holds significant promise for remote sensing estimation of Rs. The use of SIF can enhance the accuracy of Rs estimation. Full article

Accurate prediction of electricity demand during hot seasons is essential for maintaining power system reliability, particularly in humid subtropical regions such as Guangdong, China, where high temperatures strongly influence consumption. However, many models rely primarily on air temperature and may not fully capture combined atmospheric effects. This study evaluates the potential of biometeorological thermal indices for improving summer electricity load forecasting. Daily maximum load and meteorological data during May–September 2019–2021 were analyzed using Back-Propagation Neural Network (BP), Random Forest (RF), and a Stacking ensemble model. Three indices—Effective Temperature (ET), Physiological Equivalent Temperature (PET), and the Universal Thermal Climate Index (UTCI)—were introduced as predictors. The ensemble model achieved the best performance, with Ensemble–UTCI yielding the highest accuracy (R² = 0.559, RMSE = 60.96 × 10⁴ kW, MAE = 45.10 × 10⁴ kW). Compared with temperature-based models, biometeorological indices consistently improved predictions, with UTCI performing best (average RMSE = 62.81 × 10⁴ kW). Bayesian analysis shows strong evidence of improvement in RF and ensemble models, but not in BP or linear models, indicating model dependence. During the July 2021 heat event, RF showed greater robustness, with PET–RF achieving the lowest error (MAPE = 3.03%). These results demonstrate the value of biometeorological indices for load forecasting in humid subtropical regions. Full article

Digital management of modern railway bridges, a substantial part of high-speed railway networks, is often hindered by manual interpretation of construction drawings for Building Information Modeling (BIM). While individual technologies like optical character recognition (OCR) and neural networks are well-established, their generic application often fails on complex engineering documents. To address this, a domain-adaptive automatic recognition and semantic interpretation framework is proposed for railway bridge construction drawings. The novelty of this work lies in a specialized hybrid data fusion strategy that intelligently merges vector CAD file parsing with morphology-denoised OCR, resolving spatial and semantic conflicts. Furthermore, a back-propagation (BP) neural network is explicitly adapted to classify the extracted text into specific engineering categories, overcoming the challenges of dense layouts and overlapping symbols. Finally, the framework achieves end-to-end integration by transforming these semantic entities directly into structured, IFC-compatible BIM parameters. Evaluated on 250 real-world drawings, the framework achieved an average F1-score of 91.0% in semantic classification and improved processing efficiency by 6.5 times compared to manual methods. Moreover, 93.8% of the extracted entities achieved strict BIM parameter correctness, defined as seamless mapping to Revit IFC attributes without manual intervention. Full article