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KR-102964981-B1 - Engineering optimization system

KR102964981B1KR 102964981 B1KR102964981 B1KR 102964981B1KR-102964981-B1

Abstract

The present invention relates to an engineering optimization system, and more specifically, to a technology for performing structural analysis simulations based on digital drawing data of a building or facility. In particular, the present invention relates to an engineering optimization system that improves the reliability and efficiency of the design verification process by automatically determining simulation results stepwise according to objective criteria such as international standard design protocols, dynamically adjusting verification criteria according to the importance of the facility, and providing the results to the user with intuitive grades and specific feedback.

Inventors

  • 노영대

Dates

Publication Date
20260513
Application Date
20250827

Claims (6)

  1. A drawing generation module that receives drawing data of a building or facility; A verification module that performs a simulation based on the above drawing data and determines the structural safety of the above drawing data by comparing the simulation result with a preset judgment criterion; and A feedback management module that guides follow-up actions based on the above structural safety determination results; Includes, The above verification module is, A step of receiving information on the use of facilities included in the above drawing data; A step of identifying an importance grade corresponding to the received usage information by referring to a pre-established database, and dynamically querying a set of judgment criteria linked to the identified importance grade; and It is characterized by determining the structural safety by applying the set of judgment criteria dynamically retrieved above, and The above set of judgment criteria is, It includes an importance factor for correcting design loads according to the above importance grade, The above verification module is characterized by applying the retrieved importance coefficient to the load conditions of the simulation to strengthen the judgment criteria, and The above verification module is, A step of performing a first-stage strength safety determination by comparing the maximum stress extracted from the simulation results with the design strength calculated based on the material information of the drawing data and international standard design protocols; and Characterized by sequentially performing a second-stage usability safety determination by comparing the actual maximum displacement extracted from the simulation results with the allowable displacement calculated based on the use information of the drawing data and the international standard design protocol, only when the above-mentioned first-stage strength safety determination is satisfied. The above verification module is, If the maximum stress exceeds the design strength as a result of the above Stage 1 strength safety assessment, the final safety rating is determined as [Dangerous-Strength], and If the above Stage 1 is satisfied but the above actual maximum displacement exceeds the above allowable displacement as a result of the Stage 2 usability safety assessment, the final safety rating is determined as [Caution-Deformation], and Characterized by classifying the final safety rating as [Safe] when both the above Stage 1 and Stage 2 judgments are satisfied, The above feedback management module is, If the above final safety rating is determined to be [Danger-Strength] or [Caution-Deformation], a warning message including the comparison result (maximum stress and design strength value, or actual maximum displacement and allowable displacement value) that caused the determination is generated and transmitted to the user terminal, and Characterized by transmitting the drawing data to a cost calculation module that calculates construction costs, limited to the drawing data where the final safety rating is determined to be [Safe]. The above verification module is, To determine the reliability of the simulation results, a logical processing procedure using a multi-level demotion method is performed to determine the reliability grade using multiple judgment criteria that evaluate the consistency between the simulation model and the actual field, wherein The above procedure is characterized by initially setting the confidence level to the highest level, [High], and lowering the current confidence level by one step whenever an item that does not satisfy one of the above multiple judgment criteria is found. The above multiple judgment criteria are, It includes a stress distribution pattern criterion for determining whether the stress distribution in the simulation is similar to the actual phenomenon, a shape consistency criterion for determining whether the 3D shape model matches the shape of the actually constructed structure, and an environmental condition consistency criterion for determining whether the environmental variables applied to the simulation match the actual site environment, The verification module is characterized by determining whether the judgment criteria are satisfied by comparing actual stress distribution data obtained from a fiber optic sensor attached to the site or 3D point cloud data obtained from a laser scanner with the simulation results. The above verification module is, If field measurement data is not received, the above reliability rating is output as a separate status [Undetermined - No Data] to inform the user that the reliability evaluation was performed incompletely, and Characterized by generating a warning message recommending a re-examination of the simulation model and transmitting it through the feedback management module when the above reliability rating is determined to be [low], even if the above structural safety determination result is [safe]. The above verification module is, It further includes a design rule-based inference engine internally, When the above final safety rating is determined to be [Danger-Intensity] or [Caution-Deformation], the cause of the problem is identified through the inference engine, and a pre-stored set of problem-solving rules is sequentially applied to the original drawing data to automatically generate multiple modified virtual drawing data, which are solution alternatives. The above verification module is, For each of the generated plurality of solution alternatives, a multidimensional evaluation matrix is generated by evaluating a technical efficiency criterion for determining whether the safety rating is improved, a project ripple effect criterion for determining the impact on subsequent processes, a construction complexity criterion for determining the difficulty of on-site implementation, and an economic feasibility criterion for determining the additional costs incurred. The above verification module is, A step of receiving input from the user regarding the project management policy, which is the top priority management goal of the current project; and An engineering optimization system characterized by performing the step of dynamically determining the application order and criteria value of predefined filtering rules according to the input project management policy, and sequentially excluding or compressing the solution alternatives included in the multidimensional evaluation matrix to select an optimal single solution.
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Description

Engineering optimization system The present invention relates to an engineering optimization system, and more specifically, to a technology for performing structural analysis simulations based on digital drawing data of a building or facility. In particular, the present invention relates to an engineering optimization system that improves the reliability and efficiency of the design verification process by automatically determining simulation results stepwise according to objective criteria such as international standard design protocols, dynamically adjusting verification criteria according to the importance of the facility, and providing the results to the user with intuitive grades and specific feedback. Recently, as Computer-Aided Design (CAD) and Building Information Modeling (BIM) technologies have become widespread in construction engineering fields such as architecture and civil engineering, the importance of structural analysis simulations utilizing digital drawing data is increasing significantly. Conventional structural analysis systems focused on providing numerical calculation results, such as stress and displacement, based on the model and load conditions input by the designer. However, since these systems merely output calculated numerical values, designers faced the inconvenience of having to manually compare and review the results by separately consulting relevant standard manuals to determine whether the output satisfied specific regulations or design standards. During this process, there was a problem where inexperience or mistakes on the part of the designer could lead to incorrect standard analysis or the omission of certain items, potentially resulting in design defects. Furthermore, conventional systems tended to evaluate safety for all facilities using uniform standards. However, in actual design, despite the fact that the required levels of safety differ between facilities such as general warehouses and essential facilities of high social importance, such as hospitals and fire stations, there was a lack of automated functions to systematically consider this importance and apply verification standards differentially. As a result, designers had to directly calculate load factors or allowable standards appropriate for the importance of each facility and manually reflect them in the simulation conditions; this reduced work efficiency and increased the possibility of human error. Therefore, there is a need for a new engineering optimization system that goes beyond simple numerical calculation results, automatically determines safety based on objective design standards, and intelligently adjusts verification criteria according to the characteristics of the facility to provide clear and reliable feedback to the designer. Figure 1 illustrates an overall relationship diagram according to the present invention. Figure 2 illustrates a flowchart between all components according to the present invention. Figure 3 illustrates a flowchart of Process 1 according to the present invention. Figure 4 illustrates a flowchart of process 2 according to the present invention. Figure 5 illustrates a flowchart of process 3 according to the present invention. Figure 6 illustrates a flowchart of process 4 according to the present invention. Hereinafter, various embodiments are described in more detail with reference to the attached drawings. The embodiments described in this specification may be modified in various ways. Specific embodiments may be depicted in the drawings and described in detail in the detailed description. However, specific embodiments disclosed in the attached drawings are intended only to facilitate understanding of various embodiments. Accordingly, the technical concept is not limited by specific embodiments disclosed in the attached drawings, and it should be understood that it includes all equivalents or substitutions that fall within the spirit and scope of the invention. Terms including ordinal numbers, such as first, second, etc., may be used to describe various components, but these components are not limited by the aforementioned terms. The aforementioned terms are used solely for the purpose of distinguishing one component from another. Functions related to artificial intelligence according to the present disclosure are operated through a processor and memory. The processor may be composed of one or more processors. In this case, the one or more processors may be general-purpose processors such as CPUs, APs, and DSPs (Digital Signal Processors), graphics-dedicated processors such as GPUs and VPUs (Vision Processing Units), or artificial intelligence-dedicated processors such as NPUs. The one or more processors control the processing of input data according to predefined operation rules or artificial intelligence models stored in memory. Alternatively, if the one or more processors are artificial intelligence-dedicated processors, the artificial intelligence-dedicated processors ma