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• To eliminate the influence of CO 2 and H 2 O on NH 3 and NO concentration measurements, this paper proposes a machine-learning modeling and concentration measurement method using combined spectral signals from reference gases. • Before the concentration measurements, the direct absorption spectroscopy (DAS) signals of NH 3 and NO underwent noise reduction using the variational mode decomposition algorithm optimized by the artificial bee colony algorithm and wavelet threshold denoising (ABC-VMD-WTD) method. The signal-to-noise ratio (SNR) of NH 3 and NO increased from 17.2 and 46.9 before denoising to 167.8 and 532.4 after denoising. • After testing, the average relative errors of NH 3 and NO concentrations measured by the particle swarm optimization-support vector machine (PSO-SVM) model are 4.0 % and 0.8 %, respectively, with measurement precision of 0.05 ppm and 0.42 ppm, which is better than the conventional integral absorbance method. • By analyzing the Allan deviation, the minimum detection limit (MDL) for NH 3 is 16.1 ppb at an average time of 50 s, while for NO, it is 38.4 ppb at an average time of 71 s. Accurate measurements of NH 3 and NO are essential for controlling NO x emissions from coal-fired power plants, reducing ammonia slip, and improving the efficiency of selective catalytic reduction (SCR) operation. We propose a method for measuring NH 3 and NO concentrations based on machine learning algorithms to address the challenges posed by overlapping interference of CO 2 and H 2 O spectral lines in flue gas. Our approach introduces a novel method for acquiring mixture spectra for the model. Initially, we measure the spectra of individual components under different concentrations and temperatures. Subsequently, mixed spectral samples are generated by combining the measured spectra of the individual components. This approach simplifies the spectral measurement process while preserving accuracy. The particle swarm optimization support vector machine (PSO-SVM) algorithm is leveraged, providing a reliable foundation for the continuous and synchronous measurement of NH 3 and NO. Upon testing, the PSO-SVM demonstrates average relative errors of 4.0 % and 0.8 % for NH 3 and NO concentrations, respectively. The corresponding measurement precision is 0.05 ppm for NH 3 and 0.42 ppm for NO, better than the conventional integral absorbance method. The minimum detection limit (MDL) for NH 3 is 16.1 ppb at an average time of 50 s, while for NO, it is 38.4 ppb at an average time of 71 s. The methodology of this paper is expected to play an important role in reducing the influence of interfering components and improving the accuracy of field measurements. [ABSTRACT FROM AUTHOR]

• A numerical simulation method for coupled stress and seepage experiments of a rough single fracture was established; • The influence of roughness on the seepage characteristics of 3D fractures was discussed; • The fluid–structure coupling characteristics of single fracture under normal stress were analyzed; • The relationship between the gap width ratio b max / b h and the contact ratio r vc is proposed. In order to efficiently and reasonably determine the permeability coefficient of engineering rock mass, the two key problems of complex seepage field simulation and objective function solution in permeability coefficient inversion are studied and improved. Firstly, a 3D refined seepage numerical model is established considering the main fault fracture zones and dam seepage control system. Secondly, combined with the support vector machine (SVM) algorithm, the complex mapping relationship between permeability coefficient of each rock layer and impervious structure and piezometer head is constructed. Finally, the particle swarm optimization (PSO) algorithm is introduced, and then the efficient optimization of permeability coefficient inversion of engineering rock mass and impervious structure is realized based on PSO-SVM. The case study of LJH arch dam shows that the inversion calculation framework based on PSO-SVM not only completes the rapid proxy calculation of the numerical model of complex seepage field, but also realizes the accurate optimization of the inversion objective function. The simulation results under the final inversion parameters reproduce the variation characteristics of the piezometric tube water level of each typical measuring point under the change of reservoir water level. In addition, with the correlation analysis between the permeability coefficient and the measured value of the corresponding piezometer, the parameter inversion method is endowed with new application value. This study is of great significance for clarifying the seepage characteristics of complex rock mass and improving the inversion efficiency of dam seepage monitoring data. [ABSTRACT FROM AUTHOR]

• Masonry damage was analyzed deeply on both qualitative and quantitative levels. • PSO-SVM model was employed for automatic identification of damage phases of signals. • A mathematical expression between the AE parameter and the damage index was established. • A damage index-based early warning of masonry was proposed. Evaluation of masonry damage is considerably challenging because of the non-homogeneous nature of masonry materials and the rapid attenuation of signals. To this end, a methodology combining qualitative analysis employing support vector machine-based acoustic emission (AE) monitoring and quantitative analysis using AE rate process theory was proposed. In this work, firstly, the propagation attenuation characteristics of the AE amplitude and frequency were studied using pencil-lead break tests. The hybrid PSO-SVM model was employed to automatically identify the damage stages of the AE signals based on the field in-situ axial compression test data. Then, laboratory compression and shear tests on small-scale specimens were performed with AE monitoring. The Boltzmann sigmoidal modified function between relative stress and AE hits was established. Based on qualitative and quantitative analysis, a damage index-based early warning range was proposed, which can help to decide the damage level of masonry structures. The results of this paper can provide some new insight for the analysis of masonry damage. [ABSTRACT FROM AUTHOR]

• General structural health monitoring issues were resolved. • New method circumvents incomplete monitoring data and sensor placement limitations. • Particle swarm optimization and support vector machine were integrated for structural response estimation. • Response of steel structure under varying loads was obtained with high accuracy. • New equation for law of variation of response under different loads was formulated. Long-span spatial steel structures are affected by unfavourable factors during its service life, which can cause damage accumulation, and consequently hazards. The monitoring data of structural response of spatial steel is the basis for understanding structural behaviour and assessing structural safety, but limited number and location of sensors lead to incomplete response acquisition. Because structural members are connected to each other by joints, their structural responses are correlated to a certain degree. Based on this fact, in this paper, a structural response estimation method based on the mapping relationship and a correlation analysis is proposed. The mapping relationship between the measured response and the estimated response is determined by support vector machine with particle swarm optimisation under any load. From this, the method to obtain the optimal mapping relationship is established to realize the response estimation under different loads. In other words, through the correlation analysis of structural response under different loads, the measured point and its corresponding estimated points are determined according to the law of variation of the correlation, and parameters of the optimal mapping model are obtained. Based on the stress and displacement responses of the steel roof above the Shenzhen bay stadium under different wind loads, the effectiveness and feasibility of the proposed method are verified. [ABSTRACT FROM AUTHOR]