Ontological Approach for Automatic Inference of Concrete Crack Cause

The cause of cracks in concrete is traditionally estimated by analyzing information such as patterns and locations of the cracks and whether other defects are present, followed by aggregating the findings to estimate the cause. This method is highly dependent on the expert’s knowledge and experience in the process of identifying the cause of the cracks by compiling information related to the occurrence of the cracks, and it is likely that each expert will make a different diagnosis or an expert with insufficient knowledge and experience will make an inaccurate diagnosis. Therefore, we propose automated technology using the ontology to improve the consistency and accuracy of crack diagnosis results in this research. The proposed approach uses information on the crack patterns, locations, and penetration status, as well as the occurrence of other defects, to automatically infer the causes of cracks. We developed ontology that can infer the cause of cracks using the information on their appearance and applied actual cases of cracks to verify the ontological operation. In addition, the consistency and accuracy of the ontology were validated using eight actual cases of crack. The approach of this study can support expert decision-making in the crack diagnosis process, thereby reducing the possibility of various errors caused by the intervention of inaccurate judgments in the crack diagnosis process and improving the efficiency of the crack diagnosis tasks.

The Problem of Secret Intelligence

Why is intelligence so hard to define? Why is there no systematic or adequate theory of intelligence? This book argues that classic intelligence production has been premised on an ill-founded belief in an automatic inference between history and the future, and that the lack of a working theory has exacerbated this problem. The book uses classic cases of intelligence failure to demonstrate how this problem creates a restricted language in intelligence communities that undermines threat perception. From these cases it concludes that intelligence needs to be re-thought, and argues that good intelligence is the art of threat perception beyond the limits of our habitual thinking and shared experience. This book therefore argues that intelligence can never be truths, only uncertain theories about the future. Qualified intelligence work is, accordingly, ideas that lead to theories about the future. These theories should always seek to explain a comprehension of the wholeness of threats. The hypothesis derived from these theories must thereafter be tested, as tests that make the theories less uncertain. This implies that intelligence never can be anything but uncertain theories about the future that are made less uncertain through scientific, critical tests of hypotheses derived from these theories. High quality intelligence institutions conduct these tests in what is known as the intelligence cycle. This cycle works well if it mirrors good thinking.