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The Saudi 2030 Housing Program Vision aims to increase the population of Riyadh City, the capital of the Kingdom of Saudi Arabia, to between 15 and 20 million people. This paper aims to predict the demand for residential units in Riyadh City by 2030 in line with this vision.

This paper adopts a statistical modeling approach to estimate the residential demands for Riyadh City. Several population growth models, including the nonlinear quadratic polynomial spline regression model, the sigmoidal logistic power model and the exponential model, are tested and applied to Riyadh to estimate the expected population in 2030. The growth model closest to the Kingdom’s goal of reaching between 15 and 20 million people in 2030 is selected, and the paper predicts the required number of residential units for the population obtained from the selected model. Desktop database research is conducted to obtain the data required for the modeling and analytical stage.

The exponential model predicts a population of 16,476,470 in Riyadh City by 2030, and as a result, 2,636,235 household units are needed. This number of housing units required in Riyadh City exceeds the available residential units by almost 1,370,000, representing 108% of the available residential units in Riyadh in 2020.

This study provides valuable insights into the demand for residential units in Riyadh City by 2030 in line with the Saudi 2030 Housing Program Vision, filling the gap in prior research. The findings suggest that significant efforts are required to meet the housing demand in Riyadh City by 2030, and policymakers and stakeholders need to take appropriate measures to address this issue.

Since columns are critical structural elements, they shall withstand hazards without any considerable damage. In the case of a fire, although concrete has low thermal conductivity compared to other construction materials, its properties are changed at elevated temperatures. Most critically, the residual compressive strengths of reinforced concrete columns are significantly reduced after fire exposure. Validation of the worthiness of rehabilitating concrete structures after fire exposure is highly dependent on accurately determining the residual strengths of fire-damaged essential structural elements such as columns.

In this study, eight reinforced-concrete columns (200 × 200 × 1,500 mm) that were experimentally examined in a prior related study have been numerically modelled using ABAQUS software to investigate their residual compressive strengths after exposure to different durations of standard fire (i.e. one and two hours) while subjected to different applied load ratios (i.e. 20 and 40% of the compressive resistance of the column). Outcomes of the numerical simulations were verified against the prior study's experimental results.

In a subsequent phase, the results of a parametric study that has been completed as part of the current study to investigate the effects of the applied load ratios show that the application of axial load up to 80% of the compressive resistance of the column did not considerably influence the residual compressive strength of the shorter columns (i.e. 1,500 and 2,000-mm high). However, increasing the height of the column to 2,500 or 3,000 mm considerably reduced the residual compressive strength when the load ratio applied on the columns exceeded 60 and 40%, respectively. Also, when the different columns were simulated under two-hour standard fire exposure, the dominant failure was buckling rather than concrete crushing which was the typical failure mode in most columns.

The outcomes of the numerical study presented in this paper reflect the residual compressive strength of RC columns subjected to various applied load ratios and standard fire durations. Also, the parametric study conducted as part of this research on the effects of higher load ratios and greater column heights on the residual compressive strength of the fire-damaged columns is practical and efficient. The developed computer models can be beneficial to assist engineers in assessing the validity of rehabilitating concrete structures after being exposed to fire.

The compressive strength of concrete depends on many interdependent parameters; its exact prediction is not that simple because of complex processes involved in strength development. This study aims to predict the compressive strength of normal concrete and high-performance concrete using four datasets.

In this paper, five established individual Machine Learning (ML) regression models have been compared: Decision Regression Tree, Random Forest Regression, Lasso Regression, Ridge Regression and Multiple-Linear regression. Four datasets were studied, two of which are previous research datasets, and two datasets are from the sophisticated lab using five established individual ML regression models.

The five statistical indicators like coefficient of determination (R²), mean absolute error, root mean squared error, Nash–Sutcliffe efficiency and mean absolute percentage error have been used to compare the performance of the models. The models are further compared using statistical indicators with previous studies. Lastly, to understand the variable effect of the predictor, the sensitivity and parametric analysis were carried out to find the performance of the variable.

The findings of this paper will allow readers to understand the factors involved in identifying the machine learning models and concrete datasets. In so doing, we hope that this research advances the toolset needed to predict compressive strength.