###### Abstract  
Liquefied Natural Gas (LNG) is one of the major renewable energy sources and is stored and carried in a storage tank that is designed following international standards. Since LNG becomes highly unstable when it encounters oxygen in the air, a leakage from an LNG storage tank can cause a catastrophic industrial accident. Thus, the inspection of LNG storage tanks is one of the priorities to be completed before LNG is stored in a storage tank. Recently, the usage of Phased Array Ultrasonic Testing (PAUT) has been gradually increasing as the risks of RT emerge. PAUT has some obstacles to overcome in order to substitute RT, such as efficiency and accuracy. Specifically, the cost issue must be addressed. Therefore, many attempts to combine PAUT with Artificial Neural Networks (ANN) have been made. PAUT provides many types of 2D images of the inspected weldment. The S-scan is one of the 2D images provided by PAUT, and it displays the cross-sectional view of the specimen with a single transducer. The inspectors examine the S-scan image and other provided images of PAUT to detect, classify and size the flaw that exists in the weldment so that the decision of whether the inspected weldment with the flaw is acceptable can be made. Nowadays, most of the previous research on PAUT and ANN focuses on detecting and classifying the flaws in B-scan or S-scan images. However, the last step to determine the flaws' acceptability is not yet covered. In this study, the flaw acceptance criteria of PAUT in various international standards are listed. EXTENDE CIVA is used to create the PAUT S-scan images. The S-scan images are labeled with the listed acceptance criteria. Then, they are used in Mask R-CNN training. After the training, some new S-scan images with flaws are used to test the performance, and this showed 96% precision and 87% recall. With the algorithm, the acceptability of a flaw in a weldment can be determined efficiently and it will reduce the burden of PAUT usage and reduce the time required for a full-length inspection. constructed, must be inspected via NODE methods so that the possible risk of leakage is prevented. If not, there is a huge risk of catastrophic industrial accidents. The world has already witnessed serious LNG leakage accidents, such as the disaster caused by the leakage of LNG in 1944 [2]. Radiographic Testing (RT) is one of the main NDE methods used to inspect and evaluate LNG storage tanks. RT is a more efficient and practical NDE method that brings more precise and practical results than other NDE methods. However, the danger of radiation exposure when inspecting with RT has become an emerging issue recently. Phased Array Ultrasonic Testing (PAUT) is one of the advanced derivatives of the Ultrasonic Testing (UT) methods in NDE. The market share of UT has dramatically increased to 31.10% [3] in lieu of RT to avoid the risks that have emerged lately. Moreover, PAUT is an image-based testing method, similar RT. However, the accuracy and efficiency of PAUT still does not reach that of RT, and these remain as obstacles to the substitution of RT by PAUT. Compared with RT, PAUT needs more skilled and experienced operators due to the complexity in the procedure of evaluation. Using steering and focusing techniques, the ultrasonic beam can be controlled and provide many other types of image-based outcomes. However, PAUT still uses an ultrasonic beam, and ultrasonic beams are reflected, deflected, and attenuated by various geometrical aspects. These aspects make the outcome of PAUT extremely difficult to evaluate, even for skilled experts. To overcome these obstacles, many attempts to combine PAUT with Artificial Neural Networks (ANN) have been made. Figure 1 below briefly introduces the overall procedure of PAUT [4]. PAUT uses the steering and focusing of an ultrasonic beam produced by the phased arrayed elements in a single transducer to penetrate the specimen. Nowadays, the PAUT equipment usually saves the scanned data so that they can be evaluated after the scanning is done.