No abstract

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 order to solve connectivity problems in metropolitan areas, the development of underground metro lines constitutes an unquestionable requirement. However, the construction work thereof encounters unfavourable circumstances when surface excavations must be carried out that cross historical areas of the city, due to the need to control surface movements. The design of the metro in the city of Seville (Spain) from 2004 to 2006 provides a representative example of this situation and triggered major upheavals that exerted repercussions on historical buildings. For these reasons, the excavation stages of Line 1 of this metro have been simulated by numerical methods using FLAC3D software and validated with the results provided by the real conditions. Consequently, various surface settlements have been evaluated by taking not only variates of the main parameters that characterise the soil of Seville, but also of the various load situations and excavation conditions. Notable results have been achieved through calibration of 54 variants of the same model corresponding to Line 1, and their comparison with the real results obtained in nine critical areas of the itinerary. The results obtained have made it possible to determine the effects of excavation on the subsoil of the city of Seville with great accuracy, since the percentage error of calculated vertical surface movements varies from 0.1% to 5.3%.

Cutting tool wear constitutes one of the principal parameters in the processing cost of building stone. The life index of the cutting tool is obtained by evaluating the wear of diamond segments in the processing plants and examining the main parameters thereof. The purpose of this study is to determine the life index of the diamond cutting tool by considering the physico-mechanical properties of marble stones and the operational parameter of cutting speed. To this end, a dataset was provided by collecting the data from eight building stone processing plants in the provinces of Tehran, Isfahan, and Yazd of Iran. In this regard, the number of square metres of building stone that every diamond cutting tool can cut during its lifetime is defined as the cutting tool life index (TLI). After collecting the required data, SPSS software was employed for statistical analysis. The results revealed that the Brazilian tensile strength is the main parameter that affects the cutting tool life index. Linear and non-linear regression analyses were then considered for the development of predictive models for the TLI based on the Brazilian tensile strength. The performance of the developed models was subsequently examined by using three different criteria: the coefficient of determination, the variance accounted for, and the root-mean-square error. The results of this study show that the non-linear predictive model of the TLI presents a very good performance, and thus, the diamond cutting tool life index can be obtained for marble stones by considering the model developed herein.