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Project delays are the major problems tackled by the construction sector owing to the associated complexity and uncertainty in the construction activities. Artificial Intelligence (AI) models have evidenced their capacity to solve dynamic, uncertain and complex tasks. The aim of this current study is to develop a hybrid artificial intelligence model called integrative Random Forest classifier with Genetic Algorithm optimization (RF-GA) for delay problem prediction. At first, related sources and factors of delay problems are identified. A questionnaire is adopted to quantify the impact of delay sources on project performance. The developed hybrid model is trained using the collected data of the previous construction projects. The proposed RF-GA is validated against the classical version of an RF model using statistical performance measure indices. The achieved results of the developed hybrid RF-GA model revealed a good resultant performance in terms of accuracy, kappa and classification error. Based on the measured accuracy, kappa and classification error, RF-GA attained 91.67%, 87% and 8.33%, respectively. Overall, the proposed methodology indicated a robust and reliable technique for project delay prediction that is contributing to the construction project management monitoring and sustainability.

Reclaimed water irrigation is increasingly being applied to address global water scarcity, yet its long-term effects on soil nitrogen cycling and salinity dynamics, particularly in agricultural and agroforestry systems, remain complex and insufficiently understood. Understanding these impacts is crucial for developing sustainable practices that optimize resource use while ensuring the long-term health and viability of agricultural and agroforestry systems. This study employs genetic-algorithm-optimized random forest models (GA-RF1 and GA-RF2) to examine the dynamics of nitrogen indicators (NO3−-N, NH4+-N, and TN) and salinity indicators (EC and Cl−) under reclaimed water irrigation. The models achieved high predictive accuracy, with NSE values of 0.918, 0.946, 0.936, 0.967, and 0.887 for NO3−-N, NH4+-N, TN, EC, and Cl−, respectively, demonstrating their robustness. Key drivers of nitrogen indicators were identified as irrigation duration (years), fecal coliform levels, and soil depth, while salinity indicators were primarily influenced by land use type and the chemical composition of reclaimed water, including chemical oxygen demand, total phosphorus, and total nitrogen. Spatial analysis revealed significant nitrogen and salinity accumulation in surface soils with extended irrigation, particularly in farmland, where NO3−-N and NH4+-N peaked at 25 mg/kg and 15 mg/kg, respectively. EC exceeded 700 µS/cm during early irrigation stages but remained within crop tolerance levels. Conversely, grassland and woodland exhibited minimal nitrogen and salinity accumulation. These findings underscore the need for targeted management strategies to mitigate nitrogen and salinity buildup, particularly in farmland, to ensure long-term soil health and productivity under reclaimed water irrigation systems.