The problem of determining the prestressing force in the tendons of prestressed concrete structures and monitoring the non-exceedance of prestressing drops is an issue that has been addressed by many researchers over the past decades and has provided methods in this field. Today, pre-installation sensors are installed in important prestressed concrete structures to monitor prestressing loss. However, due to the unpredictability of such equipment in older structures, monitoring of these forces requires destructive or non-destructive testing but is inaccurate. Therefore, in this paper, a method is presented that without the need for these sensors and destructive tests, only by measuring static displacement, is able to detect the amount of prestressing loss in the cross-sectional tendons of a prestressed concrete beam. In this regard, an algorithm in the Python program environment based on genetic algorithm as well as modeling in the finite element analysis program is provided. The numerical example presented in this research shows that the proposed algorithm detects the values ​​of prestressing loss with good accuracy even in spite of 10% of the intentional error due to measurement. In recent years, the use of prestressing methods has become much simpler and more effective, and its materials have been optimized. Today, a high percentage of structures under construction worldwide are built using this technology, and the advance has found wide applications in the construction of office buildings, residential, commercial, parking lots, sports stadiums, concrete tanks and special structures such as piers. Therefore, in recent years, for long-term monitoring of prefabricated structures, equipment and sensors sensitive to force drop, such as fiber optic sensors and FBG sensors in the construction phase are predicted and installed in the desired locations. [13] However, since the above equipment requires a lot of money and it is not possible to use them in old structures, the need for a technique that shows the amount and location of force reduction in all tendons without using them remains. Therefore, in this paper, a method is presented that, while using the simplest tools, provides the most accurate results only by measuring static displacements under the effect of various loading scenarios and using an artificial intelligence algorithm based on genetic algorithm. The proposed method is based on computer analysis and comparison of the results of two prestressed concrete beams with the same geometry, loading and arrangement of tendons. First, a specific prestressing beam is modeled in the SAP2000 analysis program and the desired prestressing forces are applied to it, and then these forces are reduced in some of the studied tendons. This deliberate change in prestressing values ​​is considered as failure and the technique presented in this mapping tries to discover the extent and location of failure of this beam. In other words, this paper is the determination of the amount of prestressing force in prestressed concrete beams in which force measuring sensors are not predicted without the need for destructive testing and only by measuring the static displacement under load. In the form of a numerical example on a prestressed concrete beam consisting of 6 steel tendons and using a genetic algorithm, it was shown that the displacement is a function of the amount of prestressing and its location and amount of reduction by the technique used. It was correctly detected with 93% accuracy when 10% of the deliberate error due to displacement field measurement was applied. As a suggestion for future work, this research will be able to be developed in the simultaneous diagnosis of prestressing reduction and beam concrete failure.