Frequency effect, in addition to time delay effect, on the response of the frame structure to sinusoidal and earthquake wave passage excitations is studied, respectively. The dynamic equilibrium equation in terms of the displacements of horizontal DOFs for a single-span, one-story plane frame structure subjected to wave passage excitation is formulated, and the relative motion method and mode superposition method are used to solve the dynamic equilibrium equation. The analytical and semi-analytical solutions of structural responses of the frame structure to sinusoidal and earthquake wave passage excitations are given, respectively. A new cognition is obtained that the wave passage effect includes not only time delay effect but also frequency effect. The frequency effect is also the mechanism of wave passage effect for the frame structure. When the excitation frequency is within a range from a frequency slightly bigger than zero to a certain frequency less than the structural fundamental frequency, the lower the excitation frequency, the more significant wave passage effect. Earthquake wave passage effect for the frame structure depends on the low-frequency content of earthquake wave besides time delay, and the more the low-frequency content, the more significant wave passage effect.

Mine fires pose a serious threat to underground safety, and rapid prediction of fire-induced hazards is critical for timely rescue operations. Traditional numerical simulations using Computational Fluid Dynamics are accurate but time-consuming and limited to single or local tunnels. This study proposes a rapid prediction method for mine fires in a full ventilation network based on a backpropagation (BP) neural network. A 14-branch full-ventilation network mine model was established, and numerical simulations were conducted using Fire Dynamics Simulator (FDS) to generate a fire dataset. The BP neural network was constructed with combustion location, heat release rate, time, wind speed, ambient temperature, relative humidity, ambient pressure, and cross-sectional area as inputs, and the average CO concentration, temperature, visibility, and wind speed of each roadway as outputs. The trained model enables rapid prediction of mine fires under various conditions. The results show that the model achieves high prediction accuracy for temperature, visibility, and wind speed, with R² exceeding 0.99, while the CO concentration prediction yields a SMAPE of 110.40%, an RMSE of 0.43, and an MAE of 0.31. The inference time for a single prediction is approximately 3 s, much faster than traditional simulation methods. This study provides a methodological validation for rapid mine fire prediction in a full ventilation network and offers practical value for mine fire emergency decision-making and rescue operations.

Machinery condition monitoring is beneficial to equipment maintenance and has been receiving much attention from academia and industry. Machine learning, especially deep learning, has become popular for machinery condition monitoring because that can fully use available data and computational power. Since significant accidents might be caused if wrong fault alarms are given for machine condition monitoring, interpretable machine learning models, integrate signal processing knowledge to enhance trustworthiness of models, are gradually becoming a research hotspot. A previous spectrum-based and interpretable optimized weights method has been proposed to indicate faulty and fundamental frequencies when the analyzed data only contains a healthy type and a fault type. Considering that multiclass fault types are naturally met in practice, this work aims to explore the interpretable optimized weights method for multiclass fault type scenarios. Therefore, a new multiclass optimized weights spectrum (OWS) is proposed and further studied theoretically and numerically. It is found that the multiclass OWS is capable of capturing the characteristic components associated with different conditions and clearly indicating specific fault characteristic frequencies (FCFs) corresponding to each fault condition. This work can provide new insights into spectrum-based fault classification models, and the new multiclass OWS also shows great potential for practical applications.

Faults of rotating machinery parts, such as bearings and gears, usually cause impulsive fault components in vibration signals, and many signal processing methods have been proposed to extract impulsive fault components from vibration signals contaminated by random noise and interferential components. However, most of these methods can only extract one impulsive fault component with limited precision. Therefore, it is necessary to realize precise extraction and separation of multiple impulsive fault components, which is significant to accurate fault diagnosis and degradation assessment. A recently proposed impulsive mode decomposition (IMD) using a geometrical mean-based pq-mean (GM2to1) as an objective function is promising for realizing this purpose. Considering that statistical indices as an objective function are significant to the performance of a method, this work further explores the IMD and the GM2to1. First, the idea of the GM2to1 is generalized to power mean and sparsity measures, so a new family of statistical indices named cycle-embedded sparsity measures (CESMs) is proposed. Secondly, four theoretical and three numerical studies demonstrate that the proposed CESMs have good properties in quantifying weak impulsive fault components and distinguishing random impulsive noise. Thirdly, CESMs are used as a generalized objective function of the IMD for impulsive fault component extraction. Two experimental case studies demonstrate the effectiveness and superiority of the CESM-based IMD in precise extraction and separation of multiple impulsive fault components. The second case study also demonstrates the effectiveness of the IMD for compound fault diagnosis for the first time. Most importantly, in the future, the CESMs can be used as objective functions of many signal processing methods to extract fault components.

In this paper, the seismic response of long-span tensioning truss structures on soft soil foundation is analyzed, and the influence of traveling wave excitation on the relative displacement response of mid-span joints and the normal stress response of structural rods considering soil-structure interaction is studied. According to the equivalent linearization method based on linear wave method in frequency domain, a large-span tensioning truss structure model considering soil-structure interaction is established, and the seismic response under traveling wave excitation and uniform excitation is compared. According to the requirement of seismic code, multi-dimensional input of inversion bedrock ground motion is carried out. The peak relative displacement response of the mid-span nodes under traveling wave excitation is 74

To achieve rapid prediction of fire disaster factors such as temperature, visibility, and CO concentration in mine roadways during a fire, a rapid prediction model for mine roadway fire disaster factors based on the Whale Optimization Algorithm combined with Backpropagation Neural Network (WOA-BP) is proposed using numerical simulation with Fire Dynamics Simulator (FDS). A single rectangular roadway was selected as the research object, and a dataset was established through FDS numerical simulation for training and testing the BP neural network. The WOA was then integrated to optimize the prediction of fire disaster factors in mine roadways. The prediction performance and error characteristics of the WOA-BP model and the BP neural network model were compared and analyzed. The results indicate that the WOA-BP model can instantaneously simulate mine roadway fires under various conditions and rapidly and reliably predict the temperature, visibility, and CO concentration throughout the entire space and time domain of the roadway during a fire event. Compared with the BP neural network model, the WOA-BP model achieved an average reduction of 9.4, 2.1, and 1.12 in Symmetric Mean Absolute Percentage Error (SMAPE), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE), respectively, and an average increase of 13.49% in the coefficient of determination ($\mathbf{R}^{\mathbf{2}}$).

In emergencies such as fires in subway tunnels, the rapid prediction of piston winds therein is needed to provide theoretical guidance for emergency decision-making and fire rescue. Previous research has neglected the efficiency of piston wind prediction. For this reason, to improve on traditional methods, this paper uses eight different machine learning algorithms, including support vector regression (SVR), k-nearest neighbors (KNN), and extreme gradient boosting (XGBoost) et al., to construct a fast prediction model for unsteady piston winds in subway tunnels. A relatively optimal machine learning algorithm was determined through various evaluation methods, and feature importance analysis was carried out. The results show that it is feasible to utilize a machine-learning method for the fast prediction of unsteady piston winds in subway tunnels. The RF algorithm had a high prediction accuracy for unsteady piston winds, and the Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Symmetric Mean Absolute Percentage Error (SMAPE), and Coefficient of Determination (R-2) of the prediction results were 0.04, 0.05, 1.15 %, and 0.995, respectively. The influence degree of each factor on the subway tunnel piston wind, in descending order, is as follows: time t>blockage ratio beta>train running speed u>tunnel length l(tu)>train length l(tr).

The mine wind speed sensor is an important intelligent sensing equipment in the mine intelligent ventilation system that can provide accurate and key wind speed parameters for the intelligent ventilation system. The turbulent pulsation characteristics of the airflow in the underground tunnel are a major factor for the inaccurate measurement of mine wind speed. Therefore, according to the random non-stationary characteristics of a turbulent pulsation signal, a denoising method based on adaptive complete ensemble empirical mode decomposition (CEEMDAN) combined with the wavelet threshold is proposed for suppressing the turbulent pulsation noise in the wind speed signal. First, the CEEMDAN algorithm is used for decomposing the wind speed signal into a series of IMF components. Second, the continuous mean square error criterion is used for determining the high-frequency IMF components with more noise. The wavelet threshold denoising method is used for denoising the high-frequency IMF components with more noise. Finally, the denoised IMF components and remaining low-frequency IMF components are reconstructed for obtaining the denoised signal. The results of the denoising analysis of measured turbulent pulsation signals, comparative analysis of denoising of simulated turbulent pulsation signals by different joint denoising methods, and denoising analysis of actual mine wind speed sensor data indicate that the joint denoising method proposed in this study has a higher signal-to-noise ratio and lower root mean square error of the wind speed signal after denoising. Compared with the EMD-wavelet threshold and EEMD-wavelet threshold denoising methods, the denoising method proposed in this study is better and has higher denoising accuracy, which provides a new method for processing actual mine wind speed sensor data.

A new type of prefabricated beam-column connection T-joint is proposed, which connects prefabricated beams and columns through components such as high-strength bolts and embedded steel plates. The deformation and mechanical properties of this new fabricated T-joint under explosion load were studied, and its reliability was analyzed. The finite element analysis software LS-DYNA was used to establish respectively the finite element models of cast-in-place and prefabricated T-joint under the same conditions of the same size and loading mode. The failure characteristics, stress and displacement analysis of T-joints with two different construction techniques under blast load are compared and analyzed. The results show that compared with the cast-in-place joint, the local damage caused by the explosion shock wave to the new prefabricated T-joint is more serious, but there is no large area of concrete collapse. In addition, under the two different construction technologies, the change rules of the steel bar stress and the joint displacement of the T-joint are basically the same. Therefore, it can be concluded that the new prefabricated structure has good explosion resistance.

The digital twin model of mine ventilation system (DTMVS) plays an important role in intelligent safety management. However, the uncertainty of the ventilation resistance coefficient, which is the core parameter of the model, makes it challenging to accurately construct a DTMVS. In this study, Latin Hypercube Sampling (LHS) and ventilation resistance coefficient estimation models (VRCEMs) are used to analyze the uncertainty. First, the LHS method was used to explore the effect of uncertainty in the simulated airflow by continuously increasing the level of uncertainty in the ventilation resistance coefficients. Subsequently, the ventilation resistance coefficients were estimated using the VRCEMs, and the uncertainty of the ventilation resistance coefficient and the simulated airflow was analyzed. The results showed that the ventilation resistance coefficients with a 5% coefficient of variation can cause the DTMVS to lose 34% of the real airflow data points. The degree of uncertainty in the ventilation resistance coefficients estimated by the VRCEM-GA (VRCEM using genetic algorithm) and VRCEM-DE (VRCEM using differential evolution algorithm) methods was enhanced by 27.4% and 4.4%, respectively, compared with VRCEM-ES (VRCEM using evolutionary strategy algorithm). The VRCEM-ES model had the least influence on the uncertainty of the simulated airflow of DTMVS. The simulated airflow of the DTMVS constructed based on VRCEMs fluctuated normally within the confidence interval. VRCEMs had a higher sensitivity to the ventilation resistance coefficients of branches with low coefficients of variation.

The ventilation resistance coefficients of a mine is vital to ventilation system safety management, diagnosis, and intelligentization. Airflow typically serves as the basis for inverting ventilation resistance coefficients. However, the issue of non-uniqueness in conventional nonlinear optimization methods affects the accuracy of inversion. Therefore, this study introduces a novel optimization approach based on deep reinforcement learning (DRL) to invert resistance coefficients. In this methodology, inversion is regarded as a Markov decision process, with the ventilation network solving model (VNSM) embedded within the DRL environment. We design an agent utilizing deep neural networks, which dynamically adjusts the resistance coefficient state variables by interacting with the VNSM to enhance the consistency between theoretical and measured airflow. The consistency corresponds to the agent’s reward. The proximal policy optimization is employed to optimize the agent’s policy. In field experiment, the MAE between the airflow calculated and the measured airflow is 0.354, with MSE of 0.287, RMSE of 0.536, and MRE of 0.013. Compared with the standard genetic algorithm, differential evolution algorithm, and evolution strategy algorithm, the DRL method shows lower MRE, MAE, RMSE, and MSE values by 23.5%, 15.3%, 14.1%, and 26.4%, respectively. Additionally, DRL method exhibits smaller sensitivity differences for different roadways than others algorithm.

In this paper, the fire resistance limit state of a new bolted prefabricated beam-to-beam joint exposed to fire, and the influencing factors of its fire resistance limit were studied. Based on the ABAQUS finite element software, a numerical simulation of bolted prefabricated beam-beam joints under temperature load and static load is carried out, and the mid-span deflection curve is drawn according to the fire exposure time for different components. The mid-span deflection of the bolted prefabricated beam-beam joints gradually increases with the increase of fire exposure time, and breaks away from the linear growth when approaching the fire resistance limit. With the increase of fire exposure time, the growth rate of the mid-span deflection of the components is constantly increasing, and the growth rate of the mid-span deflection of specimens with different fire resistance is also different, the greater the load ratio of the components, the shorter their fire resistance limit; the higher the concrete strength grade, the thicker the concrete protective layer, and the longer the fire resistance limit. The size of the bolt pretightening force and the bolt strength grade have no effect on the fire resistance rating in the present model design.

In this paper, the vortex-induced vibrations of a long flexible pipe conveying steady fluid are investigated via a two-mode discretization of the governing differential equations. The governing nonlinear partial differential equations (PDEs) are transformed into ordinary differential equations (ODEs) by applying the Galerkin’s method, which are then studied numerically for the pipe with primary resonances during lock-in for each of the first two modes. The method of multiple scales is utilized to obtain the steady-state responses of the coupled equations. It is found that the frequency-amplitude relationships present typical nonlinear phenomena, including jumping and multi-value. Numerical integrations are directly implemented in the vibration equations to verify the aforementioned analytical results. Furthermore, an analytical expression that predicts the lock-in phenomenon range of external fluid velocity is derived. The influences of the velocity of internal and external fluids on the dynamical characteristics are discussed in detail. It is shown that the values of the external fluid velocity triggering the start and stop of the lock-in phenomenon will change with the value of the internal fluid velocity of the pipe.

In order to further improve the technical advantages of lightweight prefabricated concrete stairs, a kind of prefabricated stair system using a special-shaped hollow landing slab was proposed. Based on the detailed structural composition display, the design method for the main components (prefabricated flight and special-shaped prefabricated hollow landing slab) was proposed and a design application example was provided. Furthermore, specialized experimental and numerical simulation studies were conducted on the key component—the special-shaped prefabricated hollow landing slab. The research results indicated that this new kind of lightweight prefabricated concrete stairs using a special-shaped prefabricated hollow landing slab has reasonable construction, an effective design method, a clear force transmission mechanism, moderate component weight, and high transportation and installation convenience.

In this paper, a new type of prefabricated beam-column joint is proposed, which uses high-strength bolts and embedded steel plates to connect the prefabricated beam-column. The finite element software ABAQUS is used to establish the frame structure model of the new prefabricated beam-column joint, and then two natural ground motion records and one artificial ground motion record are selected to carry out the time history analysis under the action of rare earthquakes. The cast-in-place frame structure model with the same size and loading mode is established for comparative analysis. The core components of the new prefabricated beam-column joints have not yielded to damage under three ground motion records. The damage is mainly concentrated in the beam ends, which meets the design principle of ‘strong joints and weak components’. The inter-story shear force, vertex acceleration, and inter-story displacement angle of the prefabricated structure under the action of different ground motion records are slightly lower than those of the cast-in-place structure. The results show that the new prefabricated beam-column joints have good seismic performance, which meets the requirements of seismic code, and its overall seismic performance is slightly better than that of the cast-in-place structure.

To promote the application of steel-reinforced-concrete structures in prefabricated concrete structures, the semi-prefabricated steel-tube double-layer concrete (SPSTDC) column was proposed. The eccentric compression mechanical behavior was studied through theoretical analysis, experiments, and numerical simulations. Three possible failure modes, namely, compression failure, total yield failure, and tension failure, were obtained. Then, corresponding calculation methods of the bearing capacity (Nu) were established and their effectiveness was verified through comparison with test results. The analysis results showed that Nu decreased with the increase in the eccentricity. Nu increased with the precast concrete strength, and its effect on Nu diminished with increasing eccentricity. The post-cast concrete strength had a smaller effect on Nu than the precast concrete strength. Nu increased with the increase in the steel ratio αa, while the steel tube strength had little influence on Nu. The N–M curve of the SPSTDC column could be divided into three segments, corresponding to the three failure modes. For the compression failure mode, the ultimate load Ns decreased with the increase in the ultimate bending moment Ms. For the other two failure modes, the pattern was the opposite. The eccentricity had a significant impact on the horizontal lateral deflections of the SPSTDC columns. With the increase in the eccentricity, the horizontal lateral deflection corresponding to Nu increased. The horizontal lateral displacement of the mid-height section remained the largest compared with those of other sections. The lateral stiffness increased with the increase in the concrete strength, steel strength, and steel ratio. The impact of the precast concrete strength was the most significant while the influence of the steel tube strength was the weakest.

To reduce the impact of poor field connection on structural safety in prefabricated concrete structures, a new kind of prefabricated reinforced concrete structure—an FTPC (fault-tolerant prefabricated concrete) structure based on the fault-tolerant design concept—was proposed and studied in this paper. The horizontal load-bearing units of an FTPC structure are fully prefabricated or semi-prefabricated slabs. The vertical load-bearing units are formed by four types of prefabricated cantilever components. Prefabricated cantilever components are horizontally connected by welded connection and vertically connected by a specific connection method using steel tubes and cast-in-place concrete. A mathematical sampling method can be used to select several welded connection nodes as disconnected nodes to consider the actual weld quality. Enveloping design can be carried out to obtain the final design results of each cantilever component, which can realize the fault-tolerant design and better ensure the structure safety. Finite element analysis was carried out for a two-story villa as an example to verify the feasibility and rationality of an FTPC structure. Study results show that an FTPC structure can meet the requirements of safety and applicability. Moreover, it has the advantages of a flexible arrangement of load-bearing components, a clear force transmission mechanism, and a moderate component volume.

为探究风筒位置对掘进巷道风流分布规律的影响,利用Fluent软件确定出实验模型内流体进入"第二自模区"的临界风速,保证实验模型与实际巷道的流动相似,采用粒子图像测速仪(PIV)对压、抽风筒距迎头不同距离下的前压后抽式通风流场进行测量.实验结果表明:抽风筒距巷道迎头距离的改变对迎头处流场影响较小,涡流中心位置也不会发生改变,当抽风筒距迎头距离大于4.5 S,回流区的风流充分发展,流动较为平缓.压风筒距巷道迎头距离的改变对迎头处流场和涡流影响较大,当压风筒距迎头距离大于3 S,涡流中心位置向远离迎头的方向移动,涡流区域逐渐扩大.基于相似理论的PIV实验结果可为矿井掘进巷道通风工作提供一定参考.

The jacking form work is a complex system of construction environment, which is extremely important for human factors control. Establishing a harmonious man-machine-environment relationship is of great help to reduce the construction risk of jacking form work. This paper will establish the evaluation indicators which affecting man-machine-environment system balance. Then the AHP-Fuzzy evaluation method was used to carry out comprehensive evaluation. Finally, the real-life project was taken as an example to evaluate the construction safety level. The result can provide an important direction for construction safety management.