Linna Li

To address the challenge of extracting clear time-frequency features from complex shock waves in underwater borehole blasting, this study proposes an integrated CEEMDAN-VMD-SST method. The original signal is first adaptively decomposed using Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), followed by refined noise component processing through Variational Mode Decomposition (VMD). High-resolution time-frequency analysis is then achieved via the Synchrosqueezed S-Transform (SST). Experimental results demonstrate superior performance over conventional methods, with an 18.90% increase in SNR, a 41.98% reduction in reconstruction error, a 1.67% higher correlation coefficient, and a 4-fold improvement in time-frequency energy concentration. Ke y findings reveal that blasting scale dominantly affects energy distribution, with three-hole millisecond-delay blasting exhibiting significantly lower energy concentration and more dispersed time-frequency patterns than single-hole initiation. Peak pressure attenuation also varies substantially across blasting methods. This approach provides a robust tool for dynamic effect identification, parameter optimization, and safety assessment in blasting engineering.

To strengthen the safety management and control of explosives in underwater blasting construction sites, this study proposes an improved YOLOv11-based network named CRS-Y, designed to enhance the detection accuracy of explosives in complex underwater environments and improve the recognition capability of multi-scale targets. To address the limitations of traditional object detection methods in handling complex backgrounds and low-resolution targets, a lightweight re-parameterized vision transformer was integrated into the C3K module, forming a novel CSP structure (C3K-RepViT) that enhances feature extraction under small receptive fields. In combination with the Efficient Multi-Scale Attention (EMSA) mechanism, the model’s spatial feature representation is further strengthened, enabling a more effective understanding of objects in complex scenes. Furthermore, to reduce the computational cost of the P2 feature layer, a new convolutional structure named SPD-DSConv (Space-to-Depth Depthwise Separable Convolution) is proposed, which integrates downsampling and channel expansion within depthwise separable convolution. This design achieves a balance between parameter reduction and multidimensional feature learning. Finally, the Inner-IoU loss function is introduced to dynamically adjust auxiliary bounding box scales, accelerating regression convergence for both high-IoU and low-IoU samples, thereby optimizing bounding box shapes and localization accuracy while improving overall detection performance and robustness. Experimental results demonstrate that the proposed CRS-Y model achieved superior performance on the VOC2012, URPC2020, and self-constructed underwater blasting datasets, effectively meeting the real-time detection requirements of underwater blasting construction scenarios while exhibiting strong generalization ability and practical value.

Optimized earth-rock allocation is key in the construction of large-scale navigation channel projects. This paper analyzes the characteristics of a large-scale navigation channel project and establishes an earth-rock allocation system in phases and categories without a transit field. Based on the physical characteristics of the earthwork and stonework used to design a differentiated transport strategy, a synergistic optimization model is built with economic and ecological benefits. As a solution, this paper proposes a sustainable earth-rock allocation optimization method that integrates the improved ant colony algorithm and firefly algorithm, and establishes a two-stage hybrid optimization framework. The application of the Pinglu Canal Project shows that ant colony optimization via division and cooperation combined with the firefly algorithm reduces the transportation cost by 0.128% compared with traditional ant colony optimization; improves the stability by 57.46% (standard deviation) and 59.09% (coefficient of variation) compared with ant colony optimization through division and cooperation; and effectively solves the problems of precocious convergence and local optimization of large-scale earth-rock allocation. It is used to successfully construct an earth-rock allocation model that takes into account the efficiency of the project and the protection of the ecological system in a dynamic environment.

In this paper, the method of embedding piezoelectric ceramic sensors is used to test the damage of the materials and models of the urban comprehensive pipe gallery. The monitoring signal is processed by HHT method, and the frequency and energy changes of the piezoelectric signal before and after the explosion were analyzed, thus the damage characteristics of the urban comprehensive pipe gallery under the internal gas explosion are determined. The results show that in the gas explosion test inside the pipe gallery model, the waveform and peak value of the piezoelectric signal before and after the explosion did not change significantly, indicating that there was no obvious damage to the side wall of the integrated pipe gallery. However, after further time-frequency analysis of the piezoelectric signal by HHT, it is found that the dominant frequency of the piezoelectric signal after explosion has a downward trend, so it is judged that there is a small damage in the pipe gallery after explosion.

The comprehensive utility tunnel plays a crucial role in the urban development process. However, the integration of energy pipelines into the comprehensive utility tunnel can introduce a range of safety concerns. Therefore, researching the damage characteristics of urban comprehensive utility tunnels under internal gas explosions is of significant importance for optimizing tunnel design. The study involves designing and conducting explosion similarity model experiments in comprehensive utility tunnels. Through the use of piezoelectric smart aggregate monitoring technology, the tunnel model’s damage is monitored. This research aims to analyze the damage and destruction caused to the tunnel structure by internal gas explosions. It will provide a basis for determining the characteristics of tunnel damage. This paper first employs a combination of Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and an improved wavelet threshold method to denoise the monitoring signals. Next, it compares the signal strength peaks before and after the explosion to conduct an initial analysis of the tunnel’s damage situation. Finally, through Hilbert-Huang Transform (HHT) transformation, comparisons were made between the energy and frequency distribution of signals from ’RT5-Black5’ and ’RT6-Black7’ before and after two explosions, further elucidating the damage characteristics of the tunnel. Upon analysis and verification, the combination of CEEMDAN and the improved wavelet thresholding method demonstrates superior denoising effects, it is confirmed that the accumulated energy decreases and the frequency band widens after the explosion.

In order to ensure the safe operation of buried polyethylene pipelines adjacent to blasting excavations, controlling the effects of blasting vibration loads on the pipelines is a key concern. Model tests on buried polyethylene pipelines under blasting loads were designed and implemented, the vibration velocity and dynamic strain response of the pipelines were obtained using a TC-4850 blast vibrometer and a UT-3408 dynamic strain tester, and the distribution characteristics of blast vibration velocity and dynamic strain were analyzed based on the experimental data. The results show that the blast load has the greatest effect on the circumferential strain of the polyethylene pipe, and the dynamic strain response is greatest at the section of the pipe nearest to the blast source. Pipe peak vibration velocity (PPVV), ground peak particle velocity (GPPV), and the peak dynamic strain of the pipe were highly positively correlated, which verifies the feasibility of using GPPV to characterize pipeline vibration and strain level. According to the failure criteria and relevant codes, combined with the analysis of experimental results, the safety threshold of additional circumferential stress on the pipeline is 1.52 MPa, and the safety control vibration speed of the ground surface is 21.6 cm/s.

Precise and efficient air quality prediction plays a vital role in safeguarding public health and informing policy-making. Fine particulate matter, specifically PM_2.5 and PM₁₀, serves as a crucial indicator for assessing and managing air pollution levels. In this paper, a daily pollution concentration prediction model combining successive variational mode decomposition (SVMD) and a bidirectional long short-term memory (BiLSTM) neural network is proposed. Firstly, SVMD is used as an unsupervised feature-learning method to divide data into intrinsic mode functions (IMFs) and to extract frequency features and improve short-term trend prediction. Secondly, the BiLSTM network is introduced for supervised learning to capture small changes in the air pollutant sequence and perform prediction of the decomposed sequence. Furthermore, the Bayesian optimization (BO) algorithm is employed to identify the optimal key parameters of the BiLSTM model. Lastly, the predicted values are reconstructed to generate the final prediction results for the daily PM_2.5 and PM₁₀ datasets. The prediction performance of the proposed model is validated using the daily PM_2.5 and PM₁₀ datasets collected from the China Environmental Monitoring Center in Tianshui, Gansu, and Wuhan, Hubei. The results show that SVMD can smooth the original series more effectively than other decomposition methods, and that the BO-BiLSTM method is better than other LSTM-based models, thereby proving that the proposed model has excellent feasibility and accuracy.

The safety of such a high-security structure as a deep-water explosion test vessel in service is still in the exploration stage. The reliability of the vessel needs to be analyzed in order to prevent the underwater explosion shock wave and other explosion products on the test equipment causing great damage to the experimental personnel. The safety of such a high-security structure as a deep-water explosion test vessel in service has gradually attracted the attention of scholars. The reliability of the vessel needs to be analyzed in order to prevent the shock wave of underwater explosion and other explosion products on the test equipment causing great damage to the experimental personnel. In this paper, the dynamic response test data of a deepwater explosion test vessel in service under different conditions and the Elman neural network are used to establish the dynamic response prediction model of the deepwater explosion test vessel, and using the established model to make dynamic response prediction in the next experiment; the vessel yield strength and modulus of elasticity are taken as random variables, and the container dynamic strain prediction interval is the interval variable, the random-interval reliability model is established by using the interval variable and random variable. The random variables of the model are transformed into interval variables, and the interval variables are fuzzified using the affiliation function to calculate the reliability index. Since the interval variable obtained from the model will change with the change of the container dynamic test data, the interval reliability index calculated by the stochastic-interval reliability analysis model can quantify the reliability of the container and can be used as a reference for the subsequent use of the container by reducing the reliability index to calculate the service life and drug filling amount.

Although the use of polyethylene (PE) pipelines has become increasingly widespread in recent years, few studies have addressed their seismic design and ability to withstand blast waves. In order to establish their seismic capacity, the dynamic response of buried pipelines subjected to blast waves must be explored in depth. Here, we studied the dynamic response of PE pipes situated near an explosive source. Time histories of dynamic strains were measured by conventional strain gauges after simple waterproof treatment, and pipe and ground vibration velocity curves were obtained. Based on the experimental data, the attenuation law of the peak strains under the conditions of different charge masses and blast center distances was analyzed, and the spectrum characteristics of strain, velocity of the pipe, and ground velocity were studied. The results revealed that a large hoop strain on the PE pipes was produced due to the local impact near the explosive source. We found that peak hoop strain (PHS) or peak axial strain (PAS) had a power attenuation relationship with the scaled distance, and this relationship could also be derived by dimensional analysis. The average frequency of strains had the same attenuation form as the charge mass, which was between 10 Hz and 50 Hz. Additionally, the vibration of the pipe showed a low frequency. We also determined that the attenuation of the average frequency of pipe and ground vibration velocity was closely related to the charge mass and the scaled distance. Pipe peak vibration velocity (PPVV), ground peak particle velocity (GPPV), and the peak dynamic strain of pipe were highly positively correlated, which verifies the feasibility of using GPPV to characterize pipeline vibration and strain level. Thus, a blasting criterion of 10% minimum request strength (MRS) for PE pipe was proposed, which means that the additional PHS or PAS of the dangerous point must be less than 10% MRS, and we also propose limiting the safety distance–charge mass for blasts near buried PE pipelines by the criterion. Some results in this paper can serve as the basis for future in-depth theoretical research.