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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.

On June 24, 2021, a 40-year-old reinforced concrete flat plate structure building in Miami suffered a sudden partial collapse. This study analyzed the overall performance and key components of the collapsed building based on the building design codes (ACI-318 and GB 50010). Punching shear and post-punching performances of typical slab-column joints are also studied through the refined finite element analysis. The collapse process was simulated and visualized using a physics engine. By way of these analyses, weak design points of the collapsed building are highlighted. The differences between the reinforcement detailing of the collapsed building and the requirements of the current Chinese code are discussed, together with a comparison of the punching shear and post-punching performances. The simulated collapse procedure and debris distribution are compared with the actual collapse scenes.

Fiber reinforced polymer (FRP) serves as a prospective alternative to reinforcement in concrete slabs. However, similarly to traditional reinforced concrete slabs, FRP reinforced concrete slabs are susceptible to punching shear failure. Accounts of the insufficient consideration of impact factors, existing empirical models and design provisions for punching strength of FRP reinforced concrete slabs have some problems such as high bias and variance. This study established machine learning-based models to accurately predict the punching shear strength of FRP reinforced concrete slabs. A database of 121 groups of experimental results of FRP reinforced concrete slabs are collected from a literature review. Several machine learning algorithms, such as artificial neural network, support vector machine, decision tree, and adaptive boosting, are selected to build models and compare the performance between them. To demonstrate the predicted accuracy of machine learning, this paper also introduces 6 empirical models and design codes for comparative analysis. The comparative results demonstrate that adaptive boosting has the highest predicted precision, in which the root mean squared error, mean absolute error and coefficient of determination of which are 29.83, 23.00 and 0.99, respectively. GB 50010-2010 (2015) has the best predicted performance among these empirical models and design codes, and ACI 318-19 has the similar result. In addition, among these empirical models, the model proposed by El-Ghandour et al. (1999) has the highest predicted accuracy. According to the results obtained above, SHapley Additive exPlanation (SHAP) is adopted to illustrate the predicted process of AdaBoost. SHAP not only provides global and individual interpretations, but also carries out feature dependency analysis for each input variable. The interpretation results of the model reflect the importance and contribution of the factors that influence the punching shear strength in the machine learning model.

Punching is an important process of the production for ultra-high-strength steel automobile components. By selecting the appropriate punching parameters, the punching quality can be effectively improved and the punching force can be reduced. In this study, several typical fracture damage models applicable to the punching process of high strength steel were compared, where the critical damage values were calculated through an iterative predictor–corrector approach. Meanwhile, the effects of punching parameters on maximum punching force (MPF) and punched face quality for hot stamped ultra-high strength steel Usibor1500P were investigated experimentally. The results show that the Oyane damage model is the most suitable one in simulating the punching process of Usibor1500P, which can better predict the punched profile features and the variation of punching force within one stroke. The increase in die clearance decreases the MPF, while the plate thickness, punch diameter and punch corner radius increase the MPF. The change of the punch corner radius has the most significant effect on the improvement of the punched face quality.

In this paper, an effective ultrasonic-assisted micro-punching method for thin stainless sheet metal with molten plastic as flexible punch is adopted. The minimum micro hole and arrays with diameter of 0.3 mm were punched on rolled state thin stainless sheet metal with a thickness of 10 μm. The deformation mechanism and forming parameter quality were investigated. The experimental results show that cylinder pressure, ultrasonic power, and ultrasonic vibration time were the key parameters which affect each other. The ultrasonic vibration time needed to be properly set up according to the preset cylinder pressure and ultrasonic power to form a complete punching hole and avoid defects or cracks of the parts. For the micro hole with a diameter of 0.6 mm punched on a thin stainless sheet metal with a thickness of 30 μm, there are no significant effects on the qualities of the punching fracture surfaces when changing the punching parameters in the range of the cylinder pressure of 0.3–0.5 MPa and the ultrasonic power of 60–90%. In the case of cylinder pressure 0.5 MPa and ultrasonic power 75%, the width of shearing zone is between 10 and 29%, and the surface roughness Ra of the shearing zone is in the range of 0.10–0.20 μm. Our results could throw light on improving the fracture surface quality of micro-punched part obtained by ultrasonic micro punching with flexible punch.

A dynamic punching shear model is presented for general sudden column removal cases which was validated against data from a purpose-built full-scale two-story reinforced concrete building subjected to a sudden corner column removal. Such analyses are generally performed in structural robustness or integrity design against progressive collapse, and several simplifications are generally adopted to avoid complex dynamic non-linear analyses. These simplifications are generally on the conservative side and punching can be predicted incorrectly. The test results presented showed that Vierendeel action at small deformations was predominant after column removal. The dynamic amplification of the deformations and shear was significant, although punching did not occur as predicted by the model. It was found that in general cases, punching around edge columns after sudden corner column removal was not critical using design accidental load combinations, although a dynamic punching check is still needed, especially for higher live loads and low flexural and punching reinforcement ratios. Keywords: alternative load path; column removal; dynamic punching shear; flat slab; progressive collapse; structural integrity.

A detailed investigation of the ACI 445-fib punching shear database studied the effect of flexural reinforcement ratio on the punching shear strength of nonprestressed slabs. The ACI 318-19 expressions for the two-way shear strength of nonprestressed slabs do not directly include the flexural reinforcement ratio. The experimental data shows that this simplification can lead to unconservative predictions of shear strength for slabs with low flexural reinforcement ratios. ACI 318-19 introduced a minimum flexural reinforcement area requirement for two-way slabs to address this concern. Based on a review of the data, this study proposes modified expressions to directly incorporate the flexural reinforcement ratio p in the design two-way shear strength of nonprestressed slabs. This approach provides safer strength predictions for slabs with low reinforcement ratios, which can be critically important when evaluating existing structures. The proposed equations, in conjunction with the ACI 318-19 minimum flexural reinforcement requirement, can also promote safer designs for two-way slabs.

Ten high-strength concrete slabs reinforced with a new type of steel fiber, double-hooked-end steel fibers, were tested under punching shear loads. The strength of the concrete fc' varied from 80 to 100 MPa (11,600 to 14,500 psi). The fiber content Vf varied from 0 to 1.2%. Two different values of flexural reinforcement ratios p (= As/bd) of 0.9% and 1.4% were chosen for this test program. The experimental results showed that the use of double-hooked-end steel fibers in concrete enhances slab performance significantly in many ways. As the fiber volume orfiber content Vf increased, the flexural stiffness of the slab throughout loading history also increased, while both the deflections and crack widths decreased considerably. At the ultimate load stage, the punching shear strength increased by up to 156% compared to non-fibrous concrete slabs. The increase in punching shear strength is significantly higher than the increase introduced by conventional single hooked-end steel fibers. The ductility of the slabs was also significantly improved.Comparisons between design methods with experimental results show that the design method from The Concrete Society's TR-34 performs very well. Another method that was based on the yield line theory overestimates the strengths of the slabs. Model Code 2010 method also overestimates the punching shear strengths. Finally, some relevant design recommendations are given.

The present research investigates the optimization of the punching process in cold forming manufacturing, focusing on enhancing tool life, reducing damage, and improving product quality. Punching, a shearing process widely used in sheet metal forming, requires careful management of process parameters to prevent tool damage, especially to the punch and die. The research explores various design modifications to the punching tool, including conical, pointed, and stepped shafts, aimed at reducing punching force and minimizing wear, fatigue, and crack formation. Using numerical simulations (ABAQUS/Explicit), the study evaluates the impact of shear angle, punch geometry, and other key parameters on the maximum punching force and stress distribution. The results show that adjusting the punch shaft shape and optimizing the shear angle can significantly decrease stress concentrations, extend tool lifespan, and improve process efficiency. This work provides valuable insights for improving punching tool designs and ensuring longer, more efficient service lives in industrial applications.

A punching test for simply estimating the tensile strength and total elongation of steel sheets and formed parts was proposed. The tensile strength and total elongation were estimated from the shear stress at the maximum punching load and percentage of the burnished depth at the sheared edge of the slug measured without cutting, respectively. For a variety of steel sheets with a range of the tensile strength from 360 to 1500 MPa, linear functions for the estimation were experimentally obtained. The correlation of the estimated tensile strength of the steel sheets with the measured one from the uniaxial tensile test was considerably high, and the correlation of the estimated total elongation was high. The distributions of tensile strength and total elongation for hot- and cold-stamped parts were estimated. The proposed punching test is available under not only a laboratory environment but also a factory environment.