SYSTEM AND METHOD FOR CONCRETE DAMAGE DETECTION AND MAINTENANCE

Abstract

SYSTEM AND METHOD FOR CONCRETE DAMAGE DETECTION AND MAINTENANCE ABSTRACT A system (100) for concrete damage detection and maintenance is disclosed. The system (100) comprises a processor (110) connected to an input unit (108) and a memory unit (106). The processor (110) receives a visual media and utilises a trained Convolutional Neural Network (CNN) model to classify the visual media for concrete scaling, spalling, or absence of scaling or spalling. The system (100) generates structural health information based on the classified visual media, and display the structural health information on a computing device (102) through a user interface (116). The CNN model is trained and validated using a dataset of prestored concrete failure visual media. The disclosed system (100) enables efficient and accurate detection of the concrete damage by leveraging advanced machine learning techniques. Claims: 10, Figures: 4 Figure 1 is selected.

Specification

Description:BACKGROUND
Field of Invention
[001] Embodiments of the present invention generally relate to a system for concrete damage detection and maintenance, particularly to a system and a method for concrete damage detection and maintenance by performing classification of concrete scaling, and spalling using a Convolutional Neural Network (CNN).
Description of Related Art
[002] Concrete, renowned for its durability and strength, is widely used in construction. However, concrete structures can deteriorate over time, exhibiting issues like scaling and spalling. The scaling refers to the flaking or peeling of the concrete surface, while the spalling involves the breaking or chipping off concrete layers. These damages not only compromise the structural integrity and aesthetics of concrete structures but also pose safety risks, necessitating prompt maintenance and repairs.
[003] Traditionally, professionals have relied on visual inspection to detect and classify concrete damage. However, this manual inspection process suffers from subjectivity, time consumption, and potential human error. Accurately identifying and categorizing various types of concrete damage often requires specialized knowledge and expertise. Moreover, the results of visual inspections are not readily comprehensible to non-engineering professionals, and create communication and collaboration challenges among stakeholders involved in maintenance and repair.
[004] In recent years, advances in artificial intelligence and deep learning techniques, such as Convolutional Neural Networks (CNNs), have demonstrated promise in automating the identification and classification of diverse visual patterns and objects. Presently, there is no existing system that adequately addresses the challenges associated with automating concrete damage detection and maintenance using Convolutional Neural Networks (CNNs). While commercial inspection systems have been developed to detect defects in various materials, including scaling and spalling. These solutions often rely on multiple technologies and lack a specific focus on the automated classification of concrete damage using deep learning techniques. Consequently, these traditional approaches fail to fully harness the potential of CNNs and other artificial intelligence techniques to accurately classify and analyze concrete scaling, spalling, and the absence of the scaling and the spalling. Consequently, a significant market gap exists for a comprehensive system that combines the power of CNNs, trained models, and dedicated software applications to automate the detection and classification of concrete damage.
[005] There is thus a need for an improved and advanced system for concrete damage detection and maintenance that can administer the aforementioned limitations in a more efficient manner.
SUMMARY
[006] Embodiments in accordance with the present invention provide system for concrete damage detection and maintenance. The system comprising: an input unit adapted to receive visual media of a concrete structure. The system further comprising: a processor connected to the input unit and the memory unit. The processor is configured to: receive the visual media of the concrete structure from the input unit; classify the received visual media for concrete scaling, spalling, and an absence of the scaling and the spalling using a trained Convolutional Neural Network (CNN) model stored in a memory unit, wherein the Convolutional Neural Network (CNN) model is trained and validated using a dataset of prestored concrete failure visual media; generate structural health information based on the classified visual media; and transmit, and display the structural health information to and/or on a computing device using a user interface.
[007] Embodiments in accordance with the present invention further provide a method for concrete damage detection and maintenance using a system. The method comprising steps of: receiving visual media of a concrete structure from an input unit; classifying the received visual media for concrete scaling, spalling, and an absence of the scaling and the spalling using a trained Convolutional Neural Network (CNN) model stored in a memory unit, wherein the Convolutional Neural Network (CNN) model is trained and validated using a dataset of prestored concrete failure visual media; generating structural health information based on the classified visual media; and transmitting, and displaying the structural health information to and/or on a computing device using a user interface.
[008] Embodiments of the present invention may provide a number of advantages depending on their particular configuration. First, embodiments of the present application may provide a system for concrete damage detection and maintenance.
[009] Next, embodiments of the present application may provide a system for concrete damage detection and maintenance that enables safety on a construction site.
[0010] Next, embodiments of the present application may provide a system for concrete damage detection and maintenance that ensures a long-time sturdiness of concrete used in construction.
[0011] Next, embodiments of the present application may provide a system for concrete damage detection and maintenance that can detect damage in concrete structures at an early stage, before it becomes a major problem.
[0012] Next, embodiments of the present application may provide a system for concrete damage detection and maintenance that encourages timely maintenance actions to prevent a need for more extensive and expensive interventions, leading to significant cost savings.
[0013] Next, embodiments of the present application may provide a system for concrete damage detection and maintenance that optimizes resource allocation by providing insights based on priority levels of maintenance tasks.
[0014] These and other advantages will be apparent from the present application of the embodiments described herein.
[0015] The preceding is a simplified summary to provide an understanding of some embodiments of the present invention. This summary is neither an extensive nor exhaustive overview of the present invention and its various embodiments. The summary presents selected concepts of the embodiments of the present invention in a simplified form as an introduction to the more detailed description presented below. As will be appreciated, other embodiments of the present invention are possible by utilizing, alone or in combination, one or more of the features set forth above or described in detail below.
BRIEF DESCRIPTION OF THE DRAWINGS
[0016] The above and still further features and advantages of embodiments of the present invention will become apparent upon consideration of the following detailed description of embodiments thereof, especially when taken in conjunction with the accompanying drawings, and wherein:
[0017] FIG. 1A illustrates a block diagram of a system for concrete damage detection and maintenance, according to an embodiment of the present invention;
[0018] FIG. 1B illustrates a user interface of the system, according to an embodiment of the present invention;
[0019] FIG. 2 illustrates a block diagram of a processor of the system, according to an embodiment of the present invention; and
[0020] FIG. 3 depicts a flowchart of a method for concrete damage detection and maintenance using the system, according to an embodiment of the present invention.
[0021] The headings used herein are for organizational purposes only and are not meant to be used to limit the scope of the description or the claims. As used throughout this application, the word "may" is used in a permissive sense (i.e., meaning having the potential to), rather than the mandatory sense (i.e., meaning must). Similarly, the words "include", "including", and "includes" mean including but not limited to. To facilitate understanding, like reference numerals have been used, where possible, to designate like elements common to the figures. Optional portions of the figures may be illustrated using dashed or dotted lines, unless the context of usage indicates otherwise.
DETAILED DESCRIPTION
[0022] The following description includes the preferred best mode of one embodiment of the present invention. It will be clear from this description of the invention that the invention is not limited to these illustrated embodiments but that the invention also includes a variety of modifications and embodiments thereto. Therefore, the present description should be seen as illustrative and not limiting. While the invention is susceptible to various modifications and alternative constructions, it should be understood, that there is no intention to limit the invention to the specific form disclosed, but, on the contrary, the invention is to cover all modifications, alternative constructions, and equivalents falling within the scope of the invention as defined in the claims.
[0023] In any embodiment described herein, the open-ended terms "comprising", "comprises", and the like (which are synonymous with "including", "having" and "characterized by") may be replaced by the respective partially closed phrases "consisting essentially of", "consists essentially of", and the like or the respective closed phrases "consisting of", "consists of", the like.
[0024] As used herein, the singular forms "a", "an", and "the" designate both the singular and the plural, unless expressly stated to designate the singular only.
[0025] FIG. 1A illustrates a block diagram of a system 100 (hereinafter referred to as the system 100) for concrete damage detection and maintenance, according to an embodiment of the present invention. The system 100 may provide structural health information of a concrete structure based upon a visual media of the concrete structure, in an embodiment of the present invention. According to embodiments of the present invention, the concrete structure may be, but not limited to a building, a tower, a complex, a statue, a bridge, a dam, an infrastructure project, and so forth. Embodiments of the present invention are intended to include or otherwise cover any concrete structure, including known, related art, and/or later developed technologies. In an embodiment of the present invention, the system 100 may analyse the concrete structure constructed using materials including but not limited to aggregates a crushed stone, gravels, sand, cement, water, and additives. Embodiments of the present invention are intended to include or otherwise cover any materials for the concrete structure, including known, related art, and/or later developed technologies.
[0026] According to embodiments of the present invention, the structural health information may comprise information such as, but not limited to, classification results, a maintenance schedule, and so forth. The system 100 may further provide supplementary information that may, but not limited to, historical maintenance records, visual documentation of damage, repair recommendations, cost estimations, expert analysis reports, and so forth. Embodiments of the present invention are intended to include or otherwise cover any information that may be provided in the structural health information by the system 100, including known, related art, and/or later developed technologies.
[0027] According to embodiments of the present invention, the system 100 may be installed in locations such as, but not limited to, a construction site, a renovation site, a manufacturing plant, and so forth. In another embodiment of the present invention, the system may be deployed remotely to allow monitoring and analysis of needs of the structural health information and maintenance from a centralized or an off-site location. This remote capability of the system 100 may enable an efficient and timely assessment, regardless of a physical proximity to the concrete structure. Embodiments of the present invention are intended to include or otherwise cover any location for installation of the system 100, including known, related art, and/or later developed technologies.
[0028] According to an embodiment of the present invention, the system 100 comprises a computing device 102. In an embodiment of the present invention, the computing device 102 may be a device utilized by a user to provide the visual media of the concrete structure from the input unit. The computing device 102 may further display the structural health information, in an embodiment of the present invention. The computing device 102 may be, but not limited to, a personal computer, a consumer device, and alike. Embodiments of the present invention are intended to include or otherwise cover any type of the computing device 102 including known, related art, and/or later developed technologies.
[0029] In an embodiment of the present invention, the personal computer may be, but not limited to, a desktop, a server, a laptop, and alike. Embodiments of the present invention are intended to include or otherwise cover any type of the personal computer including known, related art, and/or later developed technologies.
[0030] Further, in an embodiment of the present invention, the consumer device may be, but not limited to, a tablet, a mobile phone, a notebook, a netbook, a smartphone, a wearable device, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the consumer device including known, related art, and/or later developed technologies.
[0031] According to an embodiment of the present invention, the computing device 102 may comprise software applications such as, but not limited to, a measure application, a construction application, an appointment application, and the like. In a preferred embodiment of the present invention, the computing device 102 may provide the user interface which may be a computer-readable program installed in the computing device 102 for executing functions associated with the system 100.
[0032] The computing device 102 may further comprise an imaging unit 104, a memory unit 106, an input unit 108, a processor 110, an image processing unit 112, a decision making unit 114, a user interface 116, and a communication unit 118.
[0033] In an embodiment of the present invention, the imaging unit 104 may be configured to capture the visual media of the concrete structure. According to embodiments of the present invention, the visual media may be, but not limited to, a still image, a captured image, an uploaded visual media, a video footage of the concrete structure, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the visual media of the concrete structure, including known, related art, and/or later developed technologies.
[0034] According to the other embodiments of the present invention, the imaging unit 104 may be, but not limited to, a still camera, a video camera, a color balancer camera, a thermal camera, an infrared camera, a telephoto camera, a wide-angle camera, a macro camera, a Close-Circuit Television (CCTV) camera, a web camera, and so forth. In a preferred embodiment of the present invention, the imaging unit 104 may be an action camera. Embodiments of the present invention are intended to include or otherwise cover any type of the imaging unit 104, including known, related art, and/or later developed technologies.
[0035] According to other embodiments of the present invention, a resolution for the captured visual media of the concrete structure using the imaging unit 104 may be in a range from 320 pixels by 240 pixels to 1920 pixels by 1080 pixels. Embodiments of the present invention are intended to include or otherwise cover any resolution for the captured visual media of the concrete structure using the imaging unit 104, including known, related art, and/or later developed technologies.
[0036] In an embodiment of the present invention, the captured visual media of the concrete structure may further be stored in the memory unit 106. The memory unit 106 may further store other visual media of the concrete structure that may not be captured by the imaging unit 104 of the system 100 but may be stored in the memory unit 106 by the user, in an embodiment of the present invention. In an embodiment of the present invention, the memory unit 106 may be a non-transitory storage medium. In an embodiment of the present invention, non-limiting examples of the memory unit 106 may be a Read Only Memory (ROM), a Random-Access Memory (RAM), an Erasable Programmable Read Only Memory (EPROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a hard drive, a removable media drive for handling memory cards, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the memory unit 106, including known, related art, and/or later developed technologies.
[0037] In an embodiment of the present invention, the input unit 108 may be adapted to receive visual media of the concrete structure. The input unit 108 may be configured to receive the visual media selected from sources, in an embodiment of the present invention. According to embodiments of the present invention, the sources may be, but not limited to, the imaging unit 104 integrated into the computing device 102.
[0038] According to a further embodiment of the present invention, the sources may be, but not limited to, the memory unit 106. The memory unit 106 may refer to a storage component within the system 100 that may be capable of storing and retrieving data. In an embodiment of the present invention, the memory unit 106 may store the visual media that may be previously captured or acquired. The memory unit 106 may make the visual media readily accessible for analysis and processing.
[0039] According to other embodiments of the present invention, the sources may be, but not limited to, a network connection for receiving the visual media from external sources. In such an embodiment of the present invention, the input unit 108 may be connected to a network that may allow the input unit 108 to receive the visual media from the external sources that may be remote cameras or other imaging devices located in different areas or connected to the network.
[0040] According to yet other embodiments of the present invention, the sources may be, but not limited to, a file sharing application for retrieving the visual media from cloud storage, and so forth. Embodiments of the present invention are intended to include or otherwise cover any source for receipt of the visual media to the input unit 108, including known, related art, and/or later developed technologies.
[0041] In an embodiment of the present invention, the processor 110 may be connected to the input unit 108 and the memory unit 106. The processor 110 may further be configured to execute the computer-readable instructions to generate an output relating to the system 100. According to embodiments of the present invention, the processor 110 may be, but not limited to, a Programmable Logic Control (PLC) unit, a microprocessor, a development board, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of processor 110 including known, related art, and/or later developed technologies. In an embodiment of the present invention, the processor 110 may further be explained in conjunction with FIG. 2.
[0042] In an embodiment of the present invention, the image processing unit 112 may be configured to apply pre-processing techniques to the captured visual media before inputting the captured visual media to the Convolutional Neural Network (CNN) model for a classification process. The purpose of applying pre-processing techniques in image processing may be to enhance the quality of the visual media or prepare the visible media for further analysis and interpretation. By applying the pre-processing techniques, the visual media may be optimized for specific tasks such as an object recognition, a classification, a segmentation, or other analysis that may require extracting features from the visual media. The pre-processing techniques may be, but not limited to, image resizing, image cropping, image normalization, color space conversion, image enhancement, denoising, edge detection, image compression, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of pre-processing techniques implemented by the image processing unit 112 including known, related art, and/or later developed technologies.
[0043] In an embodiment of the present invention, the decision making unit 114 for evaluating a budget, budget constraints, and priority levels based on the generated structural health information to generate maintenance schedules and allocate financial resources effectively. In an embodiment of the present invention, the decision making unit 114 may utilize an AI framework (not shown) for evaluating the budget, the budget constraints, and the priority levels. The AI framework may incorporate advanced algorithms and machine learning techniques to analyze the data and make informed decisions. In an embodiment of the present invention, the decision making unit may utilize the AI framework to generate the maintenance schedules and to allocate the financial resources, effectively. The AI framework may employ predictive analytics to forecast future maintenance requirements and estimate associated costs. The decision making unit 114 may analyze historical data, patterns, and trends to identify potential risks and prioritize maintenance activities, accordingly, using the AI framework. In an embodiment of the present invention, the AI framework may consider various factors, that may be but not limited to a severity of damage, a criticality of maintenance tasks, available financial resources, and other relevant parameters.
[0044] Furthermore, in an embodiment of the present invention, the AI framework may continuously learn and adapt from new data inputs and feedback, enhancing its decision-making capabilities over time. The AI framework may incorporate feedback from maintenance personnel, performance monitoring systems, and external sources to improve the accuracy and efficiency of the decision-making process.
[0045] In an embodiment of the present invention, the user interface 116 may be configured for providing options to the user. According to embodiments of the present invention, the options provided to the user may be, but not limited to, an interaction with the system, adjusting system settings, accessing maintenance-related information, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the options that may be provided to the user via the user interface 116 (further be explained in conjunction with the FIG. 1B), including known, related art, and/or later developed technologies.
[0046] In an embodiment of the present invention, the communication unit 118 may be configured to transmit the generated structural health information in real-time to a building maintenance agency via a communication network (not shown). According to embodiments of the present invention, the communication unit 118 may utilize a communication network such as, but not limited to a wired communication network, a wireless communication network, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the communication network, including known, related art, and/or later developed technologies. According to embodiments of the present invention, the wired communication network may be enabled by means such as, but not limited to, a twisted pair cable, a co-axial cable, an Ethernet cable, a modem, a router, a switch, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the means that may enable the wired communication network, including known, related art, and/or later developed technologies.
[0047] According to embodiments of the present invention, the wireless communication network may be enabled by means such as, but not limited to, a Wi-Fi communication modem, a Bluetooth communication modem, a millimeter waves communication modem, an Ultra-High Frequency (UHF) communication modem, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the means that may enable the wireless communication network, including known, related art, and/or later developed technologies.
[0048] FIG. 1B illustrates the user interface 116 of the system 100, according to an embodiment of the present invention. The user interface 116 may display the received visual media of the concrete structure, in an embodiment of the present invention. In an embodiment of the present invention, the user interface 116 may further display the concrete scaling, spalling, and an absence of the scaling and the spalling. In an exemplary embodiment of the present invention, the received visual media of the concrete structure may be reported as scaling. The user interface 116 may include features that may allow users to visualize concrete scaling, spalling, and the absence of scaling and spalling. These features may include the ability to rotate, zoom in, and zoom out the visual media, enabling users to examine the concrete structure in detail.
[0049] In an embodiment of the present invention, the user interface 116 may provide interactive controls that may allow users to rotate the visual media, and may offer different perspectives of the concrete structure. In other embodiments of the present invention, the user interface 116 may comprise the interactive controls in a form of tabs that may be, but not limited to, a rotate tab, a zoom in tab, a zoom out tab, and so forth. The ability to zoom in and zoom out may enable users to focus on specific areas of interest or get a broader overview of the concrete structure. By utilizing the user interface 116, the users may effectively assess the presence or absence of concrete scaling and spalling. The user interface 116 may further examine the visual media, zooming in to closely inspect areas and identifying any signs of scaling or spalling. Additionally, the user interface 116 may provide an ability to rotate the visual media with a comprehensive view of the concrete structure from various angles.
[0050] FIG. 2 illustrates a block diagram of the processor 110 of the system 100, according to an embodiment of the present invention. The processor 110 may comprise the programming instructions in a form of programming modules such as a data receiving module 200, a data classification module 204, a data generation module 206, and a data display module 208.
[0051] In an embodiment of the present invention, the data receiving module 200 may be configured to receive the visual media of the concrete structure from the input unit 108. The data receiving module 200 may further transmit the received visual media of the concrete structure to the data classification module 204, in an embodiment of the present invention.
[0052] In an embodiment of the present invention, the data classification module 204 may be configured to receive the visual media of the concrete structure from the data receiving module 200. The data classification module 204 may be configured to classify the received visual media for concrete scaling, spalling, and the absence of the scaling and the spalling using a trained Convolutional Neural Network (CNN) model stored in the memory unit 106, in an embodiment of the present invention. In an embodiment of the present invention, the Convolutional Neural Network (CNN) model is trained and validated using a dataset of prestored concrete failure visual media. During the classification process, the data classification module 204 may pass the received visual media through the trained CNN model. The CNN model may apply learned knowledge to extract relevant features and make predictions regarding the presence of the concrete scaling, the spalling, or the absence of the concrete scaling, and the spalling. The output of the classification process may provide valuable information about the condition of the concrete structure. The model may employ convolutional operations to extract local patterns and features associated with concrete scaling, spalling, and their absence. Through this process, the model may identify distinct visual cues, such as texture variations, irregular patterns, or the presence of cracks, that contribute to the classification.
[0053] In an embodiment of the present invention, pooling layers may be used to reduce the dimensionality of the features while retaining the most relevant information. The extracted features are then processed through fully connected layers that map them to specific classification categories. Based on the learned knowledge and the extracted features, the CNN model may classify the extracted features. The output of the classification process, in an embodiment of the present invention, may provide valuable insights into the condition of the concrete structure.
[0054] In another embodiment of the present invention, the data classification module 204 may be configured to detect instances when the classified visual media may not correspond to the dataset of the concrete failure visual media. In cases such as such the data classification module 204 may transmit an error signal to the data display module 208, in an embodiment of the present invention
[0055] Upon classification of the visual media of the concrete structure, the data classification module 204 may transmit a data generation signal to the data generation module 206.
[0056] In an embodiment of the present invention, the data generation module 206 may be configured to be activated upon receipt of the data generation signal from the data classification module 204. The data generation module 206 may be configured to generate structural health information based on the classified visual media, in an embodiment of the present invention. By analyzing the generate structural health information and probability scores assigned to each class, the user may gain a comprehensive understanding of the concrete structure's health and identify areas that require attention. This information, obtained through the classification process utilizing the CNN model, may facilitate timely maintenance and repair actions, ensuring the longevity and safety of the concrete structure.
[0057] According to embodiments of the present invention, the structural health information may be, but not limited to, classification results, a maintenance schedule, and so forth. Embodiments of the present invention are intended to include or otherwise cover any structural health information, including known, related art, and/or later developed technologies.
[0058] The data generation module 206 may further be configured to generate the supplementary information that may provide valuable insights and recommendations to the user. In an embodiment of the supplementary information may be generated on receiving an interest input from the user through the user interface 116.
[0059] Upon generation of the structural health information and/or the supplementary information, the data generation module 206 may be configured to transmit the structural health information and/or the supplementary information to the data display module 208.
[0060] In an embodiment of the present invention, the supplementary information may be the historical maintenance records that may provide a chronological overview of previous maintenance activities and repairs conducted on the concrete structure. This information may help in understanding the maintenance history and identifying recurring issues or patterns. In another embodiment of the present invention, the supplementary information may be the visual documentation of the damages that may include annotated images or videos highlighting the specific areas of concern. This visual documentation may assist in visualizing the detected concrete scaling, and the spalling from the structural health information and may provide a clear reference for further inspection and decision-making.
[0061] In a further embodiment of the present invention, the supplementary information may be the repair recommendations based on the structural health information. These recommendations may include suggested repair techniques, materials, or methods to address the identified issues effectively. In a further embodiment of the present invention, the supplementary information may be the cost estimations. The cost estimations may be an estimation of the financial resources required for recommended repairs. This may help in budget planning and resource allocation for the maintenance and repair actions. In a further embodiment of the present invention, the supplementary information may be the expert analysis reports that may incorporate insights and interpretations from domain experts in the field of concrete structures. These reports may provide a professional assessment of the detected concrete scaling, and the spalling and offer additional guidance and expertise in addressing the identified issues.
[0062] In an embodiment of the present invention, the data display module 208 may be configured to receive the structural health information from the data generation module 206. The data display module 208 may be configured to display the structural health information to and/or on the computing device 102 using the user interface 116, in an embodiment of the present invention.
[0063] In another embodiment of the present invention, the data display module 208 may be configured to receive the error signal from the data classification module 204. Upon receipt of the error signal, the data display module 208 may be configured to display a "no scaling and spalling" output, in an embodiment of the present invention.
[0064] FIG. 3 depicts a flowchart of a method 300 for concrete damage detection and maintenance using the system 100, according to an embodiment of the present invention.
[0065] At step 302, the system 100 may receive visual media of the concrete structure from the input unit 108.
[0066] At step 304, the system 100 may classify the received visual media for concrete scaling, spalling, and the absence of the scaling and the spalling using the trained Convolutional Neural Network (CNN) model stored in the memory unit 106. The Convolutional Neural Network (CNN) model is trained and validated using the dataset of the prestored concrete failure visual media.
[0067] At step 306, the system 100 may generate the structural health information based on the classified visual media.
[0068] At step 308, the system 100 may transmit, and display the generated structural health information to and/or on the computing device 102 using the user interface 116.
[0069] While the invention has been described in connection with what is presently considered to be the most practical and various embodiments, it is to be understood that the invention is not to be limited to the disclosed embodiments, but on the contrary, is intended to cover various modifications and equivalent arrangements included within the scope of the appended claims.
[0070] This written description uses examples to disclose the invention, including the best mode, and also to enable any person skilled in the art to practice the invention, including making and using any devices or systems and performing any incorporated methods. The patentable scope of the invention is defined in the claims, and may include other examples that occur to those skilled in the art. Such other examples are intended to be within the scope of the claims if they have structural elements that do not differ from the literal language of the claims, or if they include equivalent structural elements within substantial differences from the literal languages of the claims. , Claims:CLAIMS
I/We Claim:
1. A system (100) for concrete damage detection and maintenance, the system (100) comprising:
an input unit (108) adapted to receive visual media of a concrete structure;
a processor (110) connected to the input unit (108) and a memory unit (106), characterized in that the processor (110) is configured to:
receive the visual media of the concrete structure from the input unit (108);
classify the received visual media for concrete scaling, spalling, and an absence of the scaling and the spalling using a trained Convolutional Neural Network (CNN) model stored in the memory unit (106), wherein the Convolutional Neural Network (CNN) model is trained and validated using a dataset of prestored concrete failure visual media;
generate structural health information based on the classified visual media; and
transmit, and display the structural health information to and/or on a computing device (102) using a user interface (116).
2. The system (100) as claimed in claim 1, wherein the input unit (108) is configured to receive the visual media selected from an imaging unit (104) integrated into the computing device (102), the memory unit (106), a network connection for receiving the visual media from external sources, a file sharing application for retrieving the visual media from cloud storage, or a combination thereof.
3. The system (100) as claimed in claim 1, comprising an image processing unit (112) configured to apply pre-processing techniques to the captured visual media before inputting the captured visual media to the Convolutional Neural Network (CNN) model for classification.
4. The system (100) as claimed in claim 1, wherein the processor (110) is configured to detect instances when the classified visual media does not correspond to the dataset of concrete failure visual media, and generate a "no scaling and spalling" output.
5. The system (100) as claimed in claim 1, wherein the user interface (116) is configured for providing options for, selected from, an interaction with the system, adjusting system settings, accessing maintenance-related information, or a combination thereof.
6. The system (100) as claimed in claim 1, wherein the structural health information comprises classification results and a maintenance schedule.
7. The system (100) as claimed in claim 1, comprising a communication unit (118) to transmit the generated structural health information in real-time to a building maintenance agency via a communication network.
8. The system (100) as claimed in claim 1, comprising a decision making unit (114) for evaluating a budget, budget constraints, and priority levels based on the generated structural health information to generate maintenance schedules and allocate financial resources effectively.
9. The system (100) as claimed in claim 1, wherein the visual media is selected from a still image, a captured image, an uploaded visual media, and video footage of the concrete structure.
10. A method (300) for concrete damage detection and maintenance using a system (100), the method (300) comprising steps of:
receiving visual media of a concrete structure from an input unit (108);
classifying the received visual media for concrete scaling, spalling, and an absence of the scaling and the spalling using a trained Convolutional Neural Network (CNN) model stored in a memory unit (106), wherein the Convolutional Neural Network (CNN) model is trained and validated using a dataset of prestored concrete failure visual media;
generating structural health information based on the classified visual media; and
transmitting, and displaying the structural health information to and/or on a computing device (102) using a user interface (116).
Date: November 05, 2024
Place: Noida

Nainsi Rastogi
Patent Agent (IN/PA-2372)
Agent for the Applicant

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