For the design or assessment of concrete structures that incorporate steel fiber in their elements, the accurate prediction of the shear strength of steel fiber reinforced concrete (SFRC) beams is critical. Unfortunately, traditional empirical methods are based on a small and limited dataset, and their abilities to accurately estimate the shear strength of SFRC beams are arguable. This drawback can be reduced by developing an accurate machine learning based model. The problem with using a high accuracy machine learning (ML) model is its interpretation since it works as a black-box model that is highly sophisticated for humans to comprehend directly. For this reason, Shapley additive explanations (SHAP), one of the methods used to open a black-box machine learning model, is combined with highly accurate machine learning techniques to build an explainable ML model to predict the shear strength of SFRC slender beams. For this, a database of 330 beams with varying design attributes and geometries was developed. The new gradient boosting regression tree (GBRT) machine learning model was compared statistically to experimental data and current shear design models to evaluate its performance. The proposed GBRT model gives predictions that are very similar to the experimentally observed shear strength and has a better and unbiased predictive performance in comparison to other existing developed models. The SHAP approach shows that the beam width and effective depth are the most important factors, followed by the concrete strength and the longitudinal reinforcement ratio. In addition, the outputs are also affected by the steel fiber factor and the shear-span to effective depth ratio. The fiber tensile strength and the aggregate size have the lowest effect, with only about 1% on average to change the predicted value of the shear strength. By building an accurate ML model and by opening its black-box, future researchers can focus on some attributes rather than others.
Concrete is the most widely used building material, but it is also a recognized pollutant, causing significant issues for sustainability in terms of resource depletion, energy use, and greenhouse gas emissions. As a result, efforts should be concentrated on reducing concrete’s environmental consequences in order to increase its long-term viability. In order to design environmentally friendly concrete mixtures, this research intended to create a prediction model for the compressive strength of those mixtures. The concrete mixtures that were used in this study to build our proposed prediction model are concrete mixtures that contain both recycled aggregate concrete (RAC) and ground granulated blast-furnace slag (GGBFS). A white-box machine learning model known as multivariate polynomial regression (MPR) was developed to predict the compressive strength of eco-friendly concrete. The model was compared with the other two machine learning models, where one is also a white-box machine learning model, namely linear regression (LR), and the other is the black-box machine learning model, which is a support vector machine (SVM). The newly suggested model shows robust estimation capabilities and outperforms the other two models in terms of R2 (coefficient of determination) and RMSE (root mean absolute error) measurements.
The earthquake sequence, with a maximum earthquake magnitude of MW 3.8, that occurred during January–February 2022 at the northern Dead Sea fault, is shown to be induced by extensive groundwater abstraction in Wadi Al‐Arab basin. Wadi Al‐Arab basin, which is bordered in the west by the Dead Sea fault, has been overexploited by extensive groundwater abstraction causing significant drawdowns. Relative earthquake relocation indicates an elongated S‐N sequence subparallel to the Dead Sea fault. We simulate the three‐dimensional hydraulic head changes in the past 40 years at Wadi al Arab basin. Results show that the drawdowns at the Dead Sea fault wells reached a value greater than 180 m. We use these results to further model the poroelastic effects of the drawdown on the stability of the Dead Sea fault using a typical fault architecture including fault core surrounded by damage zone. Upward groundwater drainage through the permeable damage zone leads to compaction and strengthening. Failure on the Dead Sea fault is expected to occur on the impermeable fault core or at the protholith where weakening is expected. Groundwater abstraction in Wadi Al‐Arab basin cause changes of a few MPa in the Coulmb Failure Stress (ΔCFS) and trigger seismicity in these sections. This is the second location along the Dead Sea fault where groundwater abstraction was shown to recently induce earthquakes. With growing demand for water and long lasting droughts in the Middle East, seismicity induced by groundwater abstraction might reoccur in the near future.
The management of the available groundwater resources is vital in arid and semi-arid regions. Artificial recharging should be integrated with the groundwater resources to maintain long-term water sustainability. This study applied the cost-effective and time-saving techniques of remote sensing and GIS to delineate the groundwater recharge potential in the Al-Sarhan Basin, located in arid and semiarid regions of Jordan, by following the weighted linear combination method. The results revealed three distinct groundwater potential recharge zones (low, moderate, and high potential zones). High to moderate groundwater recharge potential zones occupied 75% of the Al-Sarhan area with considerable artificial recharge capacity because of the suitable geology, soil texture, drainage density, and flat terrain conditions. The map also depicted that 25% of the Al-Sarhan area possesses low groundwater recharging potential. The model further revealed the presence of 93% wells in potential groundwater recharge zones.
SCS synthetic unit hydrograph technic was used to estimate the storm runoff overflows entering the Housha tunnel. The flood hydrographs were derived under different storm event durations for the ungagged Husha Catchment area. Geomorphological and hydrological Parameters of the watershed were extracted using GIS measurement tools. The data identified various parameters (time to peak, time of base, and peak flow) of the synthetic unit hydrograph. The peak discharge (Qp in m3 s−1 cm−1) of rainfall excess for different time durations (5, 10, 20, 30, 60, 120, 180, 360, and 720 minutes) were estimated. The results revealed that the peak discharge (Qp) decreased with the increase in time of rainfall excess. The maximum peak discharge (27.5 in m3 s−1 cm−1) was reached after 84 minutes of beginning the rainfall storm for 5 minutes of rainfall excess whereas the minimum peak discharge (5.2 in m3 s−1 cm−1) was reached after 7 hours and 22 minutes for 12 hours of rainfall excess. GIS data-based SCS synthetic unit hydrograph model was verified by comparing the simulated runoff with the estimated runoff from measured rainfall data of the watershed.
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