Adequate cost estimation at the planning phase is an integral part of a construction project’s success. Many uncertainties disturb the planners’ initial estimations and lead to cost overruns. Although many researchers highlighted the correlation between economic conditions and construction costs, accurate quantification of the impact of this correlation has not yet been reached. This paper proposes three models that utilize artificial neural networks (ANNs) to predict the future prices of major construction materials, namely steel reinforcement bars and portland cement, in the context of the Egyptian construction industry 6 months ahead. A Microsoft Excel spreadsheet that also utilizes genetic algorithm (GA), NeuralTools software, and Python programing language in Spyder software was used to develop the three models. Historical data of steel and cement prices as well as macroeconomic indicators in Egypt from May 2008 to June 2018 were used for training, testing, and validation of the proposed models. The inputs to the proposed ANN models are the identified leading economic indicators such as gross domestic product, unemployment rate, and Consumer Price Index (CPI). The developed ANN models show promising results in prediction of month-to-month variations in material prices while having mean-absolute-percentage error that ranges from 4.0% to 11% for the different models. The proposed models can potentially be useful tools for construction contractors as well as owners for predicting and quantifying the fluctuations of major construction materials prices to prepare mitigation measures that will reduce the extra costs incurred.

The construction industry in many developing countries is considered the main engine for economic growth. Quantification of the resilience of the construction industry in developing economies is essential for stakeholders and decision makers. Many researchers have attempted to quantify the construction industry’s resilience in the context of developed economies; however, there is lack of established measures of such quantification in developing countries. This paper proposes a framework for the composition of an index that quantifies the resilience of the construction industry in developing countries. The proposed framework is demonstrated in the context of three developing countries: Rwanda, Egypt, and Turkey. The index is composed of measures such as the construction value added to a country’s gross domestic product (GDP) and employment in construction. Principal Component Analysis (PCA) is utilized for weighting and aggregation of the individual variables. Studying the causal relationship between construction growth and economic development from 1971 to 2022, results show that construction growth leads to economic development in each of the three countries. Results of the proposed index values indicate that the construction industry in each of the three countries demonstrated increased resilience by sustaining both its outputs and its employment generation aspect in the two years following the coronavirus pandemic in 2019. Quantification of the construction industry’s resilience in countries where the construction growth leads to the economic growth would provide a crucial insight for stakeholders and decision makers.

Construction material markets are often reliant on imported commodities. Import tariffs and temporary trade barriers disrupt supply chains, presenting stakeholders with uncertainties regard...