When designing flat slabs made of steel fiber-reinforced concrete (SFRC), it is very important to predict their punching shear capacity accurately. The use of machine learning seems to be a great way to improve the accuracy of empirical equations currently used in this field. Accordingly, this study utilized tree predictive models (i.e., random forest (RF), random tree (RT), and classification and regression trees (CART)) as well as a novel feature selection (FS) technique to introduce a new model capable of estimating the punching shear capacity of the SFRC flat slabs. Furthermore, to automatically create the structure of the predictive models, the current study employed a sequential algorithm of the FS model. In order to perform the training stage for the proposed models, a dataset consisting of 140 samples with six influential components (i.e., the depth of the slab, the effective depth of the slab, the length of the column, the compressive strength of the concrete, the reinforcement ratio, and the fiber volume) were collected from the relevant literature. Afterward, the sequential FS models were trained and verified using the above-mentioned database. To evaluate the accuracy of the proposed models for both testing and training datasets, various statistical indices, including the coefficient of determination (R2) and root mean square error (RMSE), were utilized. The results obtained from the experiments indicated that the FS-RT model outperformed FS-RF and FS-CART models in terms of prediction accuracy. The range of R2 and RMSE values were obtained as 0.9476–0.9831 and 14.4965–24.9310, respectively; in this regard, the FS-RT hybrid technique demonstrated the best performance. It was concluded that the three hybrid techniques proposed in this paper, i.e., FS-RT, FS-RF, and FS-CART, could be applied to predicting SFRC flat slabs.

In recent years, the monitoring range of source location technology has developed from being one-dimensional and two-dimensional to being three-dimensional. However, due to the complexity and nonuniformity of the seismic wave propagation medium and the uncertainty of the propagation law, there will be large errors in the source location results. Therefore, the analysis of vibration signal has become the key problem of current research. This paper designs a microseismic monitoring system based on Internet of Things sensors, which can monitor the vibration wave characteristics of vibration signals. In order to test the positioning accuracy of the system, this paper introduces three positioning methods: target positioning method based on time difference, time delay estimation method based on EMD, and source target positioning method based on the characteristic frequency of vibration signal. The purpose of this paper is to find the most accurate method from the three source location methods. Through these three methods, the vibration source generated by a single person walking in situ can be located in the vibration positioning experiment of human walking. The error between the actual position and the measurement source position is compared. The results show that the time delay estimation method based on empirical mode decomposition has the highest positioning accuracy. In addition, in the microseismic experiment, it is proved that the positioning accuracy of EMD using L1 norm statistical criterion is higher than that using L2 norm statistical criterion.

High-performance concrete (HPC) as a highly sophisticated aggregate in constructional projects has made modeling given mechanical properties a very complex problem. Declaring by many studies, mechanical features of HPC are not only characterized by the maximum size of coarse aggregate and water amount since influencing by the other components. Using fly-ash and silica fume as the key constituents can simultaneously increase the hardness aspects and the environmental effects. Considering the compressive strength and slump flow of concrete should be investigated before performing any practical practices. Artificial intelligence approaches with precise and low-cost methods can replace the costly experimental ways. Therefore, the present paper has aimed to link a prediction model with optimization algorithms to accurately appraise the hardness properties of HPC samples rarely found in literature like this way. In this regard, a machine learning approach of Support Vector Regression using two kernels of Gaussian and radial basis function is coupled with matheuristic algorithms to optimize the modeling process of compressive strength and slump flow of HPC samples. The internal settings of SVR would be tuned at optimal rate by optimizers to function efficiently. To investigate the performance of hybrid frameworks developed in this research, several indicators evaluated the results of hybrid models. Therefore, the R 2 of the models was calculated averagely at 0.91 with a maximum difference rate of 11% for the testing phase. While the RMSE index assessed the models with higher values of 16.56 mm for slump and 12.86 MPa for compressive strength. Generally, using smart approaches with high-accuracy performance has been proposed to be used instead of physical procedures increasing the productivity of concrete compressive strength in terms of time, energy, and cost criteria.

To improve the frequency sensitivity of a single tuned mass damper (STMD) and solve the accuracy problem of multiple tuned mass dampers (MTMD) in determining suitable installation location and effective number of tuned mass dampers (TMDs), this paper combines the construction characteristics of the rocking structures and the damping principle of TMDs to form a new type of MTMD, i.e., rocking wall tuned mass dampers (RW-TMDs). To verify the effectiveness of RW-TMDs, Finite element analyses were conducted to systematically compare the damping performance of the structure with STMD and RW-TMDs under different earthquake excitations. The results show that the RW-TMDs with different dynamic characteristics not only significantly suppress structural dynamic response but also improve the frequency sensitivity of TMD, reducing structural damage. Besides, the RW-TMDs can also improves structural inter-story deformation pattern under white noise excitation, preventing the occurrence of the layer collapse mechanism. Overall, the RW-TMDs exhibit the damping effect of the MTMD system and the advantage of rocking structures to control inter-story deformation. Therefore, the RW-TMDs possess a high potential for practical applications for new and existing buildings.

In all kinds of production, the changes of the times and scientific and technological progress make us have higher requirements for monitoring data. In order to improve the accuracy of mine rock microseismic monitoring and the sensitivity and specificity of mine disaster early warning, it is very important to use the change of light and material vibration to monitor environmental changes. This paper is the design of an optical hardware system, which improves the traditional system, uses the change of light wave to detect the spatial change, and forms a nano digital imaging photography (ndip) system to collect more detailed data. The three-year field experiment under the guidance of a simulation experiment shows that the system has higher monitoring sensitivity, early warning sensitivity, and specificity than the laser gyroscope system, but its antidust interference ability is low, so it cannot replace the laser gyroscope system in a short time, but it can supplement and realize data fusion analysis.