There are countless valuable historical monuments in Iran, whose restoration and protection are critical. Nevertheless, ignorance of the mechanical behavior of Persian adobes has jeopardized their survival. This research aims to understand the mechanical properties of historical and new Persian adobes through DTS/ITS and modulus of elasticity in tension. To this end, six groups of adobes produced in various time periods were initially collected from historical places in different parts of Iran and adobe production workshops. After preparing specimens from existing adobe materials, several mechanical strength tests, including flexural strength, splitting tensile strength (STS), and DST were conducted according to international standards. The mechanical properties and behavior of the Persian adobes were determined by analyzing the results of the experiments. Load-deflection curves obtained from flexural strength tests and stress–strain curves, stress–strain relations and normalized stress–strain relations obtained from DST test were also presented as another part of the results. Moreover, the relationships between mechanical properties were determined with high accuracy using laboratory data. The results of this research will help engineers and researchers take an effective step toward the international standardization of materials and construction related to adobe and earthen materials through perceiving the mechanical properties and developing knowledge of behavior of Persian adobes. Based on the results, new adobes exhibit higher resistance than their historical counterparts, whose mechanical strengths can be converted to each other with amplification or reduction factors. Therefore, researchers will be able to perform only one type of test to obtain mechanical strengths. Finally, a comparison was drawn between the mechanical properties of Persian adobes and those made in other countries, indicating that adobes around the world possess convergent and similar tensile behaviors. © 2025 Institution of Structural Engineers. Published by Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
