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Background: Over the last decade, a significant area of Zagros forests, especially in Ilam Province, west of Iran, has been affected by the phenomenon of oak decline. The most basic item for fighting the decline of these forests is to know site suitability, the parameters affecting it, determining the criteria affecting the decline and preparing a map of the decline potential of these forests. In this study, the efficiency of Maxent on preparing site suitability map of Dalab and Bankol and the most important criteria affecting P.Oak decline was investigated by TOPSIS. The integrated valuation of ecosystem services and tradeoffs (InVEST) was used to prepare a zoning map of decline potential of P.Oak site. Validation of maps was done with receiver operating characteristic curve (ROC) and field visits. Results: The results showed that pc-forest, distance from rivers and temperature are the most important criteria of the distribution of P.Oak. In addition, many parts of both sites were found to lack site suitability. The results of TOPSIS showed that the most important criteria of P.Oak decline are socio-economic criteria. Conclusions: Decline potential in Bankol was much higher and the pressure caused by stressful criteria was higher in this site. The results showed 6 general stresses of land use change, grazing, pests, charcoal disease, fires and tourism in two sites. Dalab has shown a better condition in terms of decline potential. The reason for this is the operation of conservation management in this site. The obtained results show the efficiency of the used methods.

Recycled aggregates (RA) are widely used around the world as an effective solution to manage construction, renovation, and demolition (CRD) waste and the depletion of natural sources. The behaviour of recycled aggregate concrete exposed to high temperatures is complex, non-linear, and affected by various parameters. In the past decade, artificial neural networks (ANNs) have been frequently used for predicting the properties of different types of concrete. This report aims to predict the compressive strength of recycled aggregate concrete when exposed to high temperatures using an ANN model. A total of 116 data points from previous experimental studies were collected for training, validating, and testing the model. The ANN-based model was developed using nine input parameters: temperature level, recycled aggregate type (siliceous and calcareous), coarse and fine recycled aggregate replacement ratio, cement and water content, and heating rate. The results indicated that the artificial neural networks model is a reliable and efficient method for predicting the compressive strength of recycled aggregate concrete after subjecting to high temperatures. It was found that recycled concrete containing siliceous aggregates and higher water-to-cement ratios has a relatively higher residual compressive strength than calcareous concrete at high temperatures.