Search Results (33)

Flexible strain sensors, fabricated from high-elongation polymers and conductive filler particles, are proving an essential tool in the study of biomechanics using wearable technology. It has been previously shown that the resistive response of such composites, relative to the amount of conductive filler material, can be reasonably modeled using a standard percolation-type model. Once a certain critical fraction of filler material is reached, a conductive network across the sample is established and resistance rapidly decreases. However, modeling the more subtle resistance changes that occur while deforming the sensors during operation is more nuanced. Conductivity across the network of particles is dominated by tunneling mechanisms at the interfaces between the filler materials. Small changes in strain at these interfaces lead to relatively large, but nevertheless continuous, changes in local resistance. By assigning some arbitrary value of resistance as a dividing line between ‘low’ and ‘high’ resistance, one might model the piezoresistive behavior using a standard percolation model. But such an assumption is likely to lead to low accuracy. Our alternative approach is to divide the range of potential resistance values into several bins (rather than the usual two bins) and apply a relatively novel multi-state percolation theory. The performance of the multi-state percolation model is assessed using a random resistor model that is assumed to provide the ground truth. The model is applied to predict resistance response with both changes in relative amount of conductive filler (i.e., to help design the initial unstrained sensor) and with applied strain (for an operating sensor). We find that a multi-state percolation model captures the behavior of the simulated composite sensor in both cases. The multicomponent percolation theory becomes more accurate with more divisions/bins of the resistance distribution, and we found good agreement with the simulation using between 10 and 20 divisions. Full article

DNS over HTTPS (DoH) is an advanced version of the traditional DNS protocol that prevents eavesdropping and man-in-the-middle attacks by encrypting queries and responses. However, it introduces new challenges such as encrypted traffic communication, masking malicious activity, tunneling attacks, and complicating intrusion detection system (IDS) packet inspection. In contrast, unencrypted packets in the traditional Non-DoH version remain vulnerable to eavesdropping, privacy breaches, and spoofing. To address these challenges, an optimized dual-path feature selection approach is designed to select the most efficient packet features for binary class (DoH-Normal, DoH-Malicious) and multiclass (Non-DoH, DoH-Normal, DoH-Malicious) classification. Ant Colony Optimization (ACO) is integrated with machine learning algorithms such as XGBoost, K-Nearest Neighbors (KNN), Random Forest (RF), and Convolutional Neural Networks (CNNs) using CIRA-CIC-DoHBrw-2020 as the benchmark dataset. Experimental results show that the proposed model selects the most effective features for both scenarios, achieving the highest detection and outperforming previous studies in IDS. The highest accuracy obtained for binary and multiclass classifications was 0.9999 and 0.9955, respectively. The optimized feature set contributed significantly to reducing computational costs and processing time across all utilized classifiers. The results provide a robust, fast, and accurate solution to challenges associated with encrypted DNS packets. Full article

The span of a double-deck cross-sea bridge that can be used for both highway and railway purposes is usually 1 to 16 km. Compared with small-span bridges and single-layer main girder forms, its lightweight design and low damping characteristics make it more prone to vortex-induced vibration (VIV). To predict the VIV performance of a double-deck steel truss (DDST) girder with additional aerodynamic measures, the VIV response of a DDST bridge was investigated using wind tunnel tests and numerical simulation, a learning sample database was established with numerical simulation results, and a prediction model for the amplitude of the DDST girder and VIV parameters was established based on three machine learning algorithms. The optimization algorithm was selected using root mean square error (RMSE) and the coefficient of determination (R²) as evaluation indices and further improved with a genetic algorithm and particle swarm optimization. The results show that for the amplitude prediction of the main girder, the backpropagation neural network model is the most effective. The most improved algorithm yields an RMSE of 0.150 and an R² of 0.9898. For the prediction of VIV parameters, the Random Forest model is the most effective. The RMSE values of the improved optimal algorithm are 0.017, 0.026, and 0.295, and the R² values are 0.9421, 0.8875, and 0.9462. The prediction model is more efficient in terms of computational efficiency compared to the numerical simulation method. Full article

Weed and disease management in organic watermelon [Citrullus lanatus (Thunb.) Matsum. & Nakai] production is challenging. Yellow nutsedge (Cyperus esculentus L.) and Palmer amaranth (Amaranthus palmeri S. Wats.) are two competitor weeds in watermelon plasticulture production systems. Anaerobic soil disinfestation (ASD) is an emerging non-chemical approach to control weeds and soilborne plant pathogens, especially in organic farming. The effect of ASD treatments on weeds and soilborne diseases is being documented on different specialty crops. However, the impact of ASD treatments on the crop and crop genotypes; specifically watermelon has not been elucidated. Therefore, the impact of chicken manure and molasses (CMM)-induced ASD on twenty commercially available watermelon genotypes/rootstocks and major weed species was evaluated in a high tunnel experiment. The experiment was constructed as a randomized complete block design with three replications. The treatments consisted of a factorial of carbon source (1) non-treated check (CK), (2) CMM by twenty watermelon genotypes and rootstock. Soil treated with carbon CMM demonstrated significantly greater cumulative anaerobicity (246,963) activity relative to CK (575,372). Under anaerobic conditions, CMM achieved 91% weed control compared to CK. A lower number of yellow nutsedge (2) and Palmer amaranth (1) counts were recorded in CMM compared to CK (8) and (28), respectively. Among watermelon genotypes, ‘Extazy’, ‘Powerhouse’, ‘Sangria’, and ‘Exclamation’ had greater vigor 8.5, 8.4, 8.4, and 8.3, respectively, at 28 days after transplanting in CMM-treated soil. Greater watermelon plant fresh biomass was recorded in CMM-treated soil for ‘Extazy’ (434 g), ‘Powerhouse’ (409 g), ‘Exclamation’ (364 g), and ‘Sangria’ (360 g). This study demonstrated the variable response of watermelon genotypes to CMM-induced ASD and provides a guide for germplasm selection in organic watermelon production under field conditions. Full article

This article investigates the design of time-accurate input signals in the angle-of-attack and pitch rate space to identify the aerodynamic characteristics of a generic triple-delta wing configuration at subsonic speeds. Regression models were created from the time history of signal simulations in DoD HPCMP CREATE^TM-AV/Kestrel software. The input signals included chirp, Schroeder, pseudorandom binary sequence (PRBS), random, and sinusoidal signals. Although similar in structure, the coefficients of these regression models were estimated based on the specific input signals. The signals covered a wide range of angle-of-attack and pitch rate space, resulting in varying regression coefficients for each signal. After creating and validating the models, they were used to predict static aerodynamic data at a wide range of angles of attack but with zero pitch rate. Next, slope coefficients and dynamic derivatives in the pitch direction were estimated from each signal. These predictions were compared with each other as well as with the ONERA wind tunnel data and some CFD calculations from the DLR TAU code provided by the NATO Science and Technology Organization research task group AVT-351. Subsequently, the models were used to predict different pitch oscillations at various mean angles of attack with given amplitudes and frequencies. Again, the model predictions were compared with wind tunnel data. Final predictions involved responses to new signals from different models. A feed-forward neural network was then used to model pressure coefficients on the upper surface of the vehicle at different spanwise sections for each signal and the validated models were used to predict pressure data at different angles of attack. Overall, the models predict similar integrated forces and moments, with the main discrepancies appearing at higher angles of attack. All models failed to predict the stall behavior observed in the measurements and CFD data. Regarding the pressure data, the PRBS signal provided the best accuracy among all the models. Full article

Rock excavation is essentially an unloading behavior, and its mechanical properties are significantly different from those under loading conditions. In response to the current deficiencies in the peak strength prediction of rocks under unloading conditions, this study proposes a hybrid learning model for the intelligent prediction of the unloading strength of rocks using simple parameters in rock unloading tests. The XGBoost technique was used to construct a model, and the PSO-XGBoost hybrid model was developed by employing particle swarm optimization (PSO) to refine the XGBoost parameters for better prediction. In order to verify the validity and accuracy of the proposed hybrid model, 134 rock sample sets containing various common rock types in rock excavation were collected from international and Chinese publications for the purpose of modeling, and the rock unloading strength prediction results were compared with those obtained by the Random Forest (RF) model, the Support Vector Machine (SVM) model, the XGBoost (XGBoost) model, and the Grid Search Method-based XGBoost (GS-XGBoost) model. Meanwhile, five statistical indicators, including the coefficient of determination (R²), mean absolute error (MAE), mean absolute percentage error (MAPE), mean square error (MSE), and root mean square error (RMSE), were calculated to check the acceptability of these models from a quantitative perspective. A review of the comparison results revealed that the proposed PSO-XGBoost hybrid model provides a better performance than the others in predicting rock unloading strength. Finally, the importance of the effect of each input feature on the generalization performance of the hybrid model was assessed. The insights garnered from this research offer a substantial reference for tunnel excavation design and other representative projects. Full article

This paper investigates the effect of spatial variability of soil elastic modulus on the longitudinal responses of the existing shield tunnel to the new tunnel undercrossing using a random two-stage analysis method (RTSAM). The Timoshenko–Winkler-based deterministic method considering longitudinal variation in the subgrade reaction coefficient and the random field of the soil elastic modulus discretized by the Karhunen–Loeve expansion method are combined to establish the RTSAM. Then, the proposed RTSAM is applied to carry out a random analysis based on an actual engineering case. Results show that the increases in the scale of fluctuation and the coefficient of variation of the soil elastic modulus lead to higher variabilities of tunnel responses. A decreasing pillar depth and mean value of the soil elastic modulus and an increasing skew angle strengthen the effect of the spatial variability of the soil elastic modulus on tunnel responses. The variabilities of tunnel responses under the random field of the soil elastic modulus are overestimated by the Euler–Bernoulli beam model. The results of this study provide references for the uncertainty analysis of the new tunneling-induced responses of the existing tunnel under the random field of soil properties. Full article

To conduct a sensitivity analysis of the relevant parameters that impact the mechanics of tunnel construction in karst areas, firstly, the orthogonal design and range analysis method is applied to sort the 11 kinds of karst-tunnel-influencing factors from high to low according to the sensitivity degree. Secondly, the random forest algorithm based on an orthogonal experimental design is applied to the feature importance ranking of the influencing factors of karst tunnels. Thirdly, according to the results of the sensitivity analysis, the optimum combinations of influencing factors of tunnel construction in karst areas is obtained. The research based on these two methods shows that when taking the vertical displacement as the target variable, the parameters with the highest feature importance are A6 (tunnel diameter) and A10 (tunnel buried depth). When taking the first principal stress as the target variable, the most important influencing factors are A10 (tunnel buried depth) and A9 (location of karst cave). When taking the principal stress difference as the target variable, the most important influencing factors are A10 (tunnel buried depth) and A6 (tunnel diameter). The level combination of the 11 influencing factors obtained by taking the principal stress difference as the target variable was more balanced than the vertical displacement and the principal stress difference as the target variables. The results of this study will provide a theoretical basis to study key parameters in the response of mechanical characteristics to the safe construction of tunnels in karst areas. Full article

Open-field tomatoes in Virginia are traditionally planted in a single row with 2 ft (0.60 m) of in-row spacing, resulting in a plant density of 4356 plants per acre (10,890 plants/ha). However, there has been increasing interest among small and medium-sized farmers in high tunnel production. In order to be profitable, farmers must maximize their yield per unit area and take advantage of the potential benefits of producing under high tunnels. A common approach under greenhouse conditions is to increase the planting density to enhance yield per area. However, high tunnel farmers often extrapolate open-field practices to their high tunnels as they believe both systems are closer related together than to greenhouse production. In those cases, high tunnel farmers could potentially be neglecting yield increases due to their planting density selection. Additionally, irrigation and fertilization management (fertigation) under high tunnels tend to be more efficient than open-field systems, as the frequency of application is increased with a lower volume per application. A higher efficiency of fertigation could alter plant yield responses, especially under traditional planting-density systems. Hence, this study aimed to identify the effect of high planting density on high tunnel tomatoes and their nutrient status on the Eastern Shore of Virginia. The experiment was established on a completely randomized block design with four replications, with 20 ft (6.09 m) experimental plots. We evaluated the combination of two in-row distances and single and double planting rows, with treatments consisting of 2 ft of in-row distance in a single row (4356 plants/acre—current open-field recommendation), 1.5 ft (0.45 m) of in-row distance in a single row (5808 plants/acre [14,520 plants/ha]), 2 ft of in-row distance in a double row (8712 plants/acre [21,780 plants/ha]), and 1.5 ft of in-row distance in a double row (11,616 plants/acre [29,040 plants/ha]). Summer-grown tomatoes produced on the Eastern Shore of Virginia under high tunnel conditions should be planted with 2 ft of in-row spacing and with a single row of plants per planting bed. Increasing the plant density or modifying the current recommended plant distribution could result in yield losses per plant between 32% and 46% and substantial increases in production costs compared with the traditional planting density. Throughout all treatments, tomato plants did not show deficient nutrient status. We hypothesized that irrigation water and pollination were the limiting factors that promoted a decrease in yield per plant for the high-density treatments. Full article

This research investigates the cross-wind aeroelastic effects of tall buildings with hexagonal cross-sections by using aeroelastic models with multiple-degree-of-freedom (MDOF). Cross-wind displacement and acceleration responses at the top of each model are measured using the wind tunnel test. The aerodynamic damping ratios for the two representative wind directions are identified by analyzing the measured responses using the random decrement technique. Results show that large-amplitude vortex-induced vibrations occur for one of the representative wind directions where the vertex of the hexagonal model is against the approaching wind, while there is no significant VIV observed for the other representative wind direction where the face of the hexagonal model is perpendicular to the approaching wind. The most dangerous wind direction is then identified based on the discussion. Two expressions for the cross-wind aerodynamic damping ratio are established for the two wind representative wind directions. The two equations can be used in engineering practice to estimate the cross-wind aerodynamic damping ratio of hexagonal tall buildings. Full article

Lightning rod structures are susceptible to wind loads due to their high slenderness ratio, high flexibility, and light weight. The wind-induced dynamic response of a lightning rod is critical for structural safety and reliability. The traditional methods for this response, including observation and simulation, focus on structural health monitoring (SHM), wind tunnel tests (WTTs), or fluid–structure interaction (FSI) simulations. However, all these approaches require considerable financial or computational investment. Additionally, problems such as data loss or data anomalies in the sensor monitoring process often occur during SHM or WTTs. This paper proposes an algorithm based on a long short-term memory (LSTM) network to predict the wind-induced dynamic response and to solve the problem of data link fracture caused by abnormal sensor data transmission or wind-induced damage to lightning rod structures under different wind speeds. The effectiveness and applicability of the proposed framework are demonstrated using actual monitoring data. Root-mean-squared error (RMSE), determination of coefficient (R²), variance accounted for (VAF), and the refined Willmott index (RWI) are employed as performance assessment indices for the proposed network model. At the same time, the random forest algorithm is adopted to analyze the correlation between the data of the different measurement points on the lightning rod structure. The results show that the LSTM method proposed in this paper has a high accuracy for the prediction of “missing” strain data during lightning rod strain monitoring under wind speeds of 15.81~31.62 m/s. Even under the extreme wind speed of 31.62 m/s, the values of RMSE, MAE, R², RWI and VAF are 0.24053, 0.18213, 0.94539, 0.88172 and 0.94444, respectively, which are within the acceptable range. Using the data feature importance analysis function, it is found that the predicted strain data of the measurement point on the top part of the lightning rod structure are closely related to the test strain data of the two adjacent sections of the structure, and the effect of the test strain data of the measurement points that are far from the predicted measurement point can be ignored. Full article

The dynamic response characteristics of a two-dimensional submerged floating tunnel (SFT) under random and freak waves were investigated in the present study. The results demonstrate that (1) the dynamic responses of the SFT under the freak wave are significantly larger than those under the largest wave in the wave train excluding the freak wave, particularly for the motion response. The maximum values of the motion responses induced by the freak wave were several times larger than those induced by the largest wave in the wave train excluding the freak wave, far exceeding the proportion of the corresponding wave height. (2) The freak wave parameter α₁ has a significant effect on the amplification coefficients of surge, heave and pitch; all increase nonlinearly as α₁ increases. Within α₁ = 1.90~2.59, the amplification coefficients of the surge, heave and pitch vary in the ranges of 1.91~6.46, 1.53~3.87 and 1.73~5.32, respectively. (3) Amplification coefficients of tension increase almost linearly as α₁ increases. Additionally, the amplification effect of the freak wave on the mooring tension is much smaller than that on motion responses. Within α₁ = 1.90~2.59, the amplification coefficients of tension vary from 1.15 to 1.35. (4) Generalised amplification coefficients of motion responses increase as α₁ increases and are all greater than 1.0, indicating that growth rates for motion responses under the freak wave exceed the growth rates for maximum wave height. Moreover, motion responses show a significantly nonlinear growth as maximum wave height increases. The generalised amplification coefficients of the mooring tension decrease as α₁ increases, and are all less than 1.0, indicating that the dynamic amplification effect of the freak wave on the mooring tension is much smaller than that on motions. On the other hand, growth rates of the mooring tension under freak waves are smaller than the linear growth rate of the height of freak waves. Full article

A freak wave is a spike in a random wave series and hence the local characteristics in the time-domain are of key importance. When freak waves act on moored floating structures, the dynamic responses of the structures in the time and frequency domains change interdependently in a short period of time. It is difficult to comprehensively and accurately describe this physical process using a single-dimensional analysis method, such as time-domain statistical analysis or frequency-domain spectral analysis. The wavelet analysis method, which can simultaneously provide the time-domain and frequency-domain joint information of the physical process, is used to discuss the time-frequency joint variation characteristics of the dynamic responses of a two-dimensional submerged floating tunnel under a freak wave. The time-frequency characteristics of the dynamic responses induced by the freak wave and the differences from the action under random waves are investigated, with a particular emphasis on the ‘convex variation’ characteristics of the dynamic responses under a freak wave. The results show that: (1) The wavelet analysis method can effectively describe the basic characteristics of the dynamic responses of the SFT under a freak wave and clearly distinguish the differences in dynamic responses under freak and random waves. (2) Freak waves have dynamic amplification effects, which are related to the freak wave parameter α₁, on a two-dimensional SFT. Following the action of freak waves on a two-dimensional SFT, significant energy concentration occurs in the time-frequency spectrum of the dynamic response in a certain time and frequency range. The degree of energy concentration increases nonlinearly with an increase in α₁, and a certain high-frequency energy appears in the time-frequency spectrum of the motion response. The maximum values of the time-frequency spectra of the dynamic responses under a freak wave are much larger than those under a random wave with the identical wave spectrum. (3) Following the action of a freak wave on a two-dimensional SFT, the generalised energy spectra of surge, heave, pitch, and mooring tensions have convex peak values, which occur simultaneous with the occurrence of the freak wave, and the convex parts significantly increase as α₁. (4) The time lengths of the influence of a freak wave on the dynamic responses exceeded the freak wave period. With an increase in α₁, the time ranges of the large values of the time-frequency spectra of surge, heave, pitch, and mooring tensions increase nearly linearly. Full article

As the number and length of high-speed railway tunnels increase in China, implicit defects such as insufficient lining thicknesses, voids, and poor compaction have become increasingly common, posing a serious threat to train operation safety. It is, therefore, imperative to conduct a comprehensive census of the defects within the tunnel linings. In response to this problem, this study proposes a high-speed railway tunnel detection method based on vehicle-mounted air-coupled GPR. Building on a forward simulation of air-coupled GPR, the study proposes the F-K filtering and BP migration algorithms based on the practical considerations of random noise and imaging interference from the inherent equipment. Through multi-dimensional quantitative comparisons, these algorithms are shown to improve the spectrum entropy values and instantaneous amplitude ratios by 4.6% and 11.6%; and 120% and 180%, respectively, over the mean and bandpass filtering algorithms, demonstrating their ability to suppress clutter and enhance the internal signal prominence of the lining. The experimental results are consistent with the forward simulation trends, and the verification using the ground-coupled GPR detection confirms that air-coupled GPR can meet the requirements of high-speed railway tunnel lining inspections. A comprehensive GPR detection model is proposed to lay the foundation for a subsequent defect census of high-speed railway tunnels. Full article

The vibration impact force from the long-term operation of a subway affects the comfort of the residents living above the superstructure and the long-term settlement deformation of the tunnel foundation. A method for evaluating the dynamic responses of superstructure–tunnel systems is important, especially because of the randomness of vibration impact force. The coupling effect of the randomness of train-vibration excitation and the nonlinearity of the geotechnical properties that are subjected to dynamic action leads to challenges in the evaluation of the performance of superstructure–underground tunnel–soil systems under train vibration. In this study, a stochastic dynamic model of subway vibration-load excitation was established; then, the time histories of samples with rich probability characteristics in the same set system were generated. According to the nonlinear dynamic finite element analysis, several nonlinear dynamic responses of the deterministic superstructure–tunnel soil-foundation system were obtained. Finally, the probabilistic performance evolution of the superstructure–tunnel soil-foundation system was obtained by integrating the first-passage and probability density evolution theories, and the long-term deformation performance of the tunnel foundation was evaluated using time-varying reliability. This study presents a novel probabilistic method and a more objective performance index for the dynamic performance assessment of superstructure–underground tunnel–soil systems that are subjected to subway-traffic excitation. Full article