Description:FIELD OF THE INVENTION

[0001] The present invention relates to a condition monitoring and repair system for constructions that performs continuous assessment of a bridge structure and accordingly controls the traffic over the bridge based on safety thresholds, while also perform automated execution of essential repair tasks over the bridge.

BACKGROUND OF THE INVENTION

[0002] Monitoring the condition of a bridge structure is crucial to ensure its safety, longevity, and functionality. Over time, bridges experience wear from environmental factors, heavy traffic, and material fatigue, which can lead to cracks, corrosion, or structural weakening. Regular condition monitoring helps detect such issues early before they escalate into serious failures. Monitoring the weight exerted on the bridge and regulating it according to the bridge's condition prevents overloading, which could worsen existing damage or lead to collapse. By identifying and limiting the stress based on real-time damage assessments, safety for all users is maintained. Timely repairs are essential to restore structural integrity, prevent accidents, and reduce long-term maintenance costs, thereby ensuring uninterrupted transportation and public safety.

[0003] Traditionally, bridge condition monitoring is performed through manual inspections by engineers or maintenance personnel. These inspections involve visual checks, hammer tapping to detect hollow sounds indicating internal voids, and basic measurement tools like rulers, calipers, or crack width gauges to monitor damage. Inspectors may also use ladders, ropes, or scaffolding to access difficult areas. Weight regulation is typically done by posting static signage with fixed load limits based on prior assessments, without real-time condition updates. These traditional methods have significant drawbacks. Visual inspections are subjective and can miss internal or early-stage defects. Accessing all structural parts manually can be risky and time-consuming. There's no real-time data on damage progression or weight stress. Load regulation lacks adaptability, failing to account for dynamic changes in bridge condition. As a result, safety may be compromised, and timely repairs are often delayed or improperly prioritized.

[0004] WO2021147242A1 discloses about a damage detection and repair apparatus for a bridge road surface comprises a vehicle body, a gantry, a positioning frame, an injection mechanism, a detection mechanism and a touch screen. The gantry is welded to the left end of the vehicle body. A horizontal plate and a servo motor are respectively fixed to upper and lower middle portions of the gantry. A ball screw is installed at an output end of the servo motor by means of a coupling. A screw nut pair is sleeved onto the ball screw. A main control device is installed at an upper left side of the positioning frame. A supporting plate is welded to each of front and rear sides of the bottom end of the positioning frame. The injection mechanism and the detection mechanism are fixed to the top end of the supporting plate from right to left. The detection and repair apparatus has a synchronous raising/lowering function and an auxiliary supporting function, and can automatically adjust repair thickness, thereby enhancing the adaptability of the apparatus to various environments, and improving repair quality.

[0005] CN213978477U discloses about the technical field of the technique of thin layer ultra-high performance concrete construction and specifically relates to an evener for ultra-high performance concrete flattening, including installing the frame on the walking track, frame bottom left and right sides all is equipped with walking wheel, walk the wheel connection in walking motor, the frame can move on the walking track through walking motor drive walking wheel motion, the frame bottom still is equipped with screed and flattening board, the left and right sides of flattening board all is connected with a flattening board elevating gear, two flattening board elevating gear drive the left and right sides of flattening board respectively and do elevating motion, the side is the symmetry respectively and is equipped with a height measuring device about the frame, two height measuring device are high settings such as with walking track top, stroke measuring device is installed to the frame lateral part. The utility model has the advantages that: the problems of poor bridge deck pavement flatness, low concrete compactness, artificial errors in manual measurement, large elevation errors and the like are solved, the overall attractiveness is achieved, the cost is reduced, and the leveling efficiency is greatly improved.

[0006] Conventionally, many systems have been developed that are capable of monitoring structural integrity and identifying damage in bridge structures. However, these existing systems lack in providing real-time autonomous decision-making for initiating preventive actions. Additionally, these existing systems also lack in integrating automated repair mechanisms and dynamic traffic regulation features that respond adaptively to varying levels of structural degradation.

[0007] In order to overcome the aforementioned drawbacks, there exists a need in the art to develop a system that is capable of continuously monitoring the structural condition of bridge infrastructure in real time, accurately detecting and classifying potential defects before they escalate into critical failures. In addition, the developed system should also autonomously perform targeted repair operations and regulate vehicular access based on structural safety conditions, thereby minimizing manual intervention, enhancing public safety, and extending the service life of the structure.

OBJECTS OF THE INVENTION

[0008] The principal object of the present invention is to overcome the disadvantages of the prior art.

[0009] An object of the present invention is to develop a system that is capable of performing real-time structural condition monitoring of bridge infrastructure, thereby ensuring timely detection of damage and anomalies.

[0010] Another object of the present invention is to develop a system that is capable of autonomously controlling vehicle entry and traffic flow over the bridge based on assessed structural risk levels, thereby enhancing bridge safety and preventing overloading during critical conditions.

[0011] Another object of the present invention is to develop a system that is capable of performing diverse maintenance operations, such as crack sealing, pothole filling, surface cleaning, and wooden plank replacement, in an automated manner, thus reducing manual intervention and increase repair responsiveness.

[0012] Another object of the present invention is to develop a system that is capable of real-time load monitoring of bridge surfaces, with automatic access restriction for overweight vehicles to prevent structural strain and fatigue.

[0013] Yet another object of the present invention is to develop a system that is capable of providing real-time visual alerts and advisory messages to bridge users, reflecting current structural condition and maintenance activity to ensure public awareness and safety.

[0014] The foregoing and other objects, features, and advantages of the present invention will become readily apparent upon further review of the following detailed description of the preferred embodiment as illustrated in the accompanying drawings.

SUMMARY OF THE INVENTION

[0015] The present invention relates to a condition monitoring and repair system for constructions that is capable of continuously assessing the integrity of bridge infrastructure for detecting strain, moisture, vibration, and surface defects. Further, the system is capable of autonomously controlling traffic flow based on detected risk levels and performing real-time maintenance tasks such as crack sealing, pothole filling, debris cleaning, and damaged plank replacement.

[0016] According to an embodiment of the present invention, a condition monitoring and repair system for constructions is disclosed comprises of Multiple interconnected monitoring sensors embedded with a bridge structure to continuously collect real-time data regarding structural condition of the bridge, a barrier assembly comprising a vertical rod and a horizontally movable rod attached via a motorized hinge joint positioned at an entry point of the bridge for controlling entry of vehicles based on structural risk levels determined from the collected data, multiple fiber optic strain sensors integrated within different layers and joints of the bridge structure for detecting strain levels to determine necessity for initiating load restriction, a grid of ultrasonic sensors and accelerometers installed underneath deck slab and piers of the bridge and integrated with ground-penetrating radar (GPR) sensors to detect abnormal vibrations, voids, or internal moisture, and generate defect reports, a set of AI-enabled imaging units and LiDAR (Light Detection and Ranging) sensors mounted along the bridge structure for capturing high-resolution visual and depth information of the surface defects, a multicolor light indicator installed at entry point of the bridge for representing real-time defect severity levels of the bridge based on cumulative sensor readings and AI classification, a display unit and a traffic light assembly installed at the bridge entry to show advisory messages including load limits, alternate routing, and repair statuses, and the traffic light controls vehicle movement.

[0017] According to another embodiment of the present invention, the present invention further comprises of an autonomous maintenance unit (AMU) mounted on a guided track laid on the bridge surface, a crack sealing unit with a turret arm and epoxy dispenser is configured with the AMU for sealing identified cracks, a pothole filling unit with a rotating chamber configured with the AMU to dispense a rapid-curing mix and a compacting roller for leveling, a cleaning unit with multiple swiveling high-pressure water jets for removing debris from the surface, a wooden plank replacement arrangement having a telescopic rod, cutting clipper, and wrench assembly configured to remove and replace the damaged planks with new planks stored in a plank holding container, a weight detection sensor is embedded in the bridge surface for detecting and measuring load applied by approaching vehicles to restrict vehicle access based on weight thresholds and cumulative bridge load, the dynamic display unit is configured to display warning messages, rerouting instructions, defect severity scores, and estimated maintenance duration in real-time, the AMU is further configured with an obstacle detection unit and a GPS module for navigating the bridge surface and avoiding collision during repair operations, the knowledge distillation-based image processing protocol enables lightweight real-time inference for surface defect classification while maintaining accuracy similar to heavier convolutional neural networks, and a holographic projection unit is installed on the AMU for projecting real-time informational signs and visual alerts onto the bridge surface during operation of the AMU.

[0018] While the invention has been described and shown with particular reference to the preferred embodiment, it will be apparent that variations might be possible that would fall within the scope of the present invention.

BRIEF DESCRIPTION OF THE DRAWINGS

[0019] These and other features, aspects, and advantages of the present invention will become better understood with regard to the following description, appended claims, and accompanying drawings where:
Figure 1 illustrates an isometric view of a condition monitoring and repair system for constructions; and
Figure 2 illustrates an isometric view of an autonomous maintenance unit (AMU) associated with the present system.

DETAILED DESCRIPTION OF THE INVENTION

[0020] 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 spirit and scope of the invention as defined in the claims.

[0021] 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.

[0022] 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.

[0023] The present invention relates to a condition monitoring and repair system for constructions that performs real-time detection and assessment of structural anomalies in bridge infrastructure. Additionally, the present invention is capable of autonomously executing maintenance operations including crack sealing, pothole filling, debris removal, and component replacement, while simultaneously regulating traffic access based on structural safety conditions.

[0024] Referring to Figure 1 and 2, an isometric view of a condition monitoring and repair system for constructions and an isometric view of an autonomous maintenance unit (AMU) associated with the present system are illustrated, respectively, comprising multiple monitoring sensors 101 embedded with a bridge structure 102, a barrier assembly 103 positioned at an entry point of the bridge and comprising a vertical rod 104 and a horizontally movable rod 105 attached via a motorized hinge joint 106, a set of AI-enabled imaging units 107 mounted along the bridge structure 102, a multicolor light indicator 108 installed at entry point of the bridge, a display unit 109 and a traffic light assembly 110 installed at the bridge entry, an autonomous maintenance unit (AMU) 111 mounted on a guided track 112 laid on the bridge surface, a crack sealing unit 201 with a turret arm 202 and epoxy dispenser 203 is configured with the AMU 111, a pothole filling unit 204 with a rotating chamber 205 is configured with the AMU 111, a compacting roller 206 for leveling, a cleaning unit 207 with multiple swiveling high-pressure water jets 208 is configured with the AMU 111, a wooden plank replacement arrangement having a telescopic rod 209, cutting clipper 210, a wrench assembly 211 is configured with the AMU 111, and a holographic projection unit 212 is installed on the AMU 111.

[0025] The system disclosed herein comprises of multiple (ranging from 8 to 10 in numbers) interconnected monitoring sensors 101 embedded at various critical regions of a bridge structure 102, such as deck slab, girders, piers, expansion joints, and support columns of the bridge structure 102. These sensors 101 are strategically positioned and function in a networked configuration to ensure comprehensive structural coverage. A user is required to activate the system manually by pressing a button installed on the bridge and linked with an inbuilt microcontroller associated with the system. The button is a type of switch that is internally connected with the system via multiple circuits that upon pressing by the user, the circuits get closed and starts conduction of electricity that tends to activate the system and vice versa.

[0026] Upon activation of the system, the sensors 101 start to operate and continuously collect real-time data that reflects the structural condition and overall condition of the bridge. The collected data includes critical metrics such as strain levels, crack propagation, vibration frequencies, load distribution, environmental effects, and potential corrosion. The monitoring sensors 101 comprises of a surface imagery sensor, depth and profile sensors, moisture sensors, piezoelectric sensors, and temperature sensors.

[0027] The surface imagery sensor is configured to capture high-resolution images of the bridge surface, for detection of visual anomalies such as cracks, surface wear, delamination, or corrosion. The surface imagery sensor consists of a high-definition camera, image processing unit, and a light source (e.g., LED or infrared). During operation, the camera captures continuous or periodic images of the bridge surface. The image processing unit, often equipped with artificial intelligence protocols, analyzes these images to identify cracks, corrosion, delamination, or surface deformation. Lighting enhances contrast and visibility in varying conditions. The data is transmitted to the microcontroller for further analysis.

[0028] The depth and profile sensors, such as laser profilers or ultrasonic depth gauges, measure deformation, depression, and surface irregularities in the structural profile, for the detection of sub-surface issues. During operation, the sensor emits a laser beam toward the surface. The reflected light is captured by a detector, and the time or angle of return is analyzed to determine the precise distance to the surface. As the sensor scans across the structure 102, it generates a detailed 3D profile or depth map. The microcontroller processes the measurements for identifying deviations such as dips, bulges, or cracks.

[0029] The moisture sensors perform real-time monitoring of internal dampness within the bridge structure 102 to detect potential water infiltration or material degradation. The moisture sensor includes capacitive or resistive sensing elements, electrodes, an insulation layer, and a wireless transmitter. The sensor is embedded within or on the surface of the bridge material. When moisture is present, it alters the dielectric constant (for capacitive sensors) or reduces resistance (for resistive sensors) between the electrodes. This change is detected and the data is processed and wirelessly transmitted to the microcontroller for analysis.

[0030] The piezoelectric sensors are employed to monitor dynamic stress and vibrations, which may be induced by vehicular loads, seismic activity, or environmental changes. These sensors help assess structural fatigue and resonance frequencies. The piezoelectric sensors consist of a piezoelectric crystal (such as quartz or PZT), electrodes, a charge amplifier, and a signal processing unit. When the sensor is exposed to dynamic forces such as stress, impact, or vibration, the deformation of the piezoelectric material generates a small electrical charge proportional to the applied force. Electrodes collect this charge and send it to the charge amplifier, which converts it into a usable voltage signal. The signal processing unit filters and analyzes the data to determine vibration frequency, amplitude, or transient loads. This information is transmitted to the microcontroller for real-time structural condition assessment.

[0031] The temperature sensors distributed across multiple locations to track thermal expansion and contraction effects, which influence stress distribution and joint behavior. The temperature sensor used herein includes a thermistor, thermocouple, or RTD (Resistance Temperature Detector), and a signal conditioning circuit. The sensor detects temperature variations by responding to changes in electrical resistance or voltage caused by ambient or material temperature shifts. For example, an RTD increases its resistance with rising temperature. This change is converted into an electrical signal by the signal conditioning circuit and sent to the microcontroller. The data helps calculate the degree of thermal expansion or contraction occurring in the structure 102.

[0032] The microcontroller processes the data received from all the sensors for structural condition monitoring and risk assessment. Upon detecting structural anomalies or elevated risk conditions such as excessive strain, critical crack propagation, or unsafe vibration levels, the microcontroller evaluates the sensors data against predefined safety thresholds. If the bridge condition is deemed unsafe or requires restricted access, the microcontroller initiates actuation of a barrier assembly 103 positioned at an entry point of the bridge, for physically blocking the path of approaching vehicles and preventing entry.

[0033] The barrier assembly 103 comprises of a vertical rod 104 rigidly affixed to the surface of the bridge structure 102 and a horizontally movable rod 105 attached to the vertical rod 104 via a motorized hinge joint 106. Upon actuation of the barrier assembly 103 by the microcontroller, the motorized hinge joint 106 tilt and deploy the horizontally movable rod 105 for controlling entry of vehicles based on structural risk levels determined from the collected data. The motorized hinge joint 106 used herein integrates an electric motor with a traditional hinge assembly to enable controlled, automated rotational movement of the horizontally movable rod 105 around a fixed axis.

[0034] The hinge joint 106 comprise of a pair of leaf that are screwed with the surface of the rods. The leafs are connected with each other by means of a cylindrical member integrated with a shaft coupled with a DC (Direct Current) motor to provide required movement to the hinge. The rotation of the shaft in clockwise and anti-clockwise direction provides required tilting movement to the horizontally movable rod 105, for creating a barrier and blocking the path of approaching vehicles and preventing entry over the bridge. Conversely, under normal or low-risk conditions, the barrier remains in the raised or retracted position, permitting uninterrupted traffic flow.

[0035] Multiple fiber optic strain sensors are embedded within various layers and structural joints of the bridge, including but not limited to the deck, piers, girders, and expansion joints. These sensors operate on the principle of light signal variation in response to mechanical deformation, for highly precise and distributed strain measurement across the entire bridge structure 102 caused by vehicular load, environmental stress, and structural fatigue.

[0036] The fiber optic strain sensor consists of optical fibers, a light source (usually a laser or LED), a photodetector, and an interrogator unit. When strain occurs due to loads or environmental factors, it causes minute deformations in the fiber, altering the light's phase, intensity, or wavelength. These changes are captured by the photodetector and analyzed by the interrogator to calculate the strain level. Each sensor transmits strain data to the microcontroller, which is programmed to analyze the input by comparing current strain values against predefined threshold limits stored in the system database. If the microcontroller detects that any region of the bridge exhibits strain values nearing or exceeding the allowable limit, the microcontroller automatically initiates the load restriction protocol in order to deploy the barrier assembly 103 to restrict access.

[0037] A grid of ultrasonic sensors and accelerometers is systematically installed beneath the deck slab and piers of the bridge structure 102. These sensors are arranged to form a synchronized diagnostic array that provides continuous real-time feedback on subsurface integrity. The ultrasonic sensors emit high-frequency sound waves into structural elements and analyze reflected signals to detect anomalies such as internal cracks, voids, delamination, and moisture intrusion. Simultaneously, the accelerometers measure subtle changes in vibrational patterns, which might indicate fatigue, impact stress, or dynamic instability under load.

[0038] To complement these sensors, ground-penetrating radar (GPR) sensors are integrated into the grid to provide depth-resolved imaging of the subsurface condition. The GPR sensors emits electromagnetic pulses that penetrate concrete and other structural materials, generating reflections from buried objects or discontinuities. Together, these sensor arrays collaborate to detect abnormalities such as voids, reinforcement corrosion, and trapped moisture, and generates an automated defect reports containing defect type, location, and severity, the report is further transmitted to the microcontroller.

[0039] A set of AI-enabled imaging units 107 and LiDAR (Light Detection and Ranging) sensors are mounted along key structural segments of the bridge, including side rails, deck surface, expansion joints, and critical stress points. The imaging units 107 are configured to capture high-resolution visual data, continuously scanning for cracks, surface wear, corrosion, spalling, and other surface abnormalities. In parallel, the LiDAR sensors emit laser pulses and measure their reflections to generate precise three-dimensional depth maps, for revealing even minute structural deformations or misalignments invisible to standard cameras.

[0040] The AI-enabled imaging units 107 comprises of a high-resolution camera lens, digital camera sensor and a processor, wherein the lens captures multiple images from different angles and perspectives with the help of digital camera sensor for providing comprehensive coverage of the bridge structure 102. The captured images then go through pre-processing steps by the processor integrated with the imaging unit. The artificial intelligence protocols integrated into the processor, including machine learning and computer vision protocols, optimize image processing by enhancing feature extraction and classification. The captured images undergo pre-processing steps such as adjusting brightness, contrast, and noise removal to enhance quality. These refined images are transmitted to the microcontroller linked with the processor in the form of electrical signals.

[0041] The LiDAR (Light Detection and Ranging) sensor sends out rapid laser pulses in a sweeping motion. These pulses travel through the air and interact with the bridge structure 102. When the laser pulses encounter the bridge structure 102, the laser bounces off from the bridge structure 102 surface. The LiDAR sensor precisely measures the time it takes for these laser pulses to travel to the bridge structure 102 surface and back to the sensor. This measurement is known as time-of-flight and as the LiDAR sensor continues to emit laser pulses and measure their time-of-flight, it creates a dense point cloud of data points. Each data point corresponds to a specific location on the drum surface. By combining the time-of-flight data from multiple laser beams at various angles, the LiDAR builds a detailed 3D (three-dimensional) map of the bridge structure 102 which is further transferred to the microcontroller linked with the LiDAR sensor.

[0042] The captured visual and depth data from the imaging units 107 and the LiDAR sensor is then processed by the microcontroller using a knowledge distillation-based image processing protocol (VGG19 to MobileNetV2) equipped with the microcontroller, for defect classification and prioritization. In this configuration, a pretrained VGG19 convolutional neural network serves as the teacher model, known for its high classification accuracy and deep feature extraction capability. The student model, MobileNetV2, is selected for its lightweight architecture and fast inference speed, making it ideal for real-time, embedded applications on the microcontroller.

[0043] The knowledge distillation process transfers the knowledge from the VGG19 teacher to the MobileNetV2 student by minimizing a loss function that includes both classification loss and the Kullback-Leibler divergence between the soft output distributions. This enables the MobileNetV2 model to mimic the predictive behavior of VGG19. As a result, the microcontroller is able to perform accurate real-time defect classification and prioritization, distinguishing between different types and severities of surface defects with a reduced computational footprint. This ensures that critical damage is flagged with higher urgency.

[0044] Further, the microcontroller activates a multicolor light indicator 108 installed prominently at the entry point of the bridge structure 102, to visually communicate the real-time structural condition status of the bridge to approaching vehicles and personnel. This indicator 108 comprises high-intensity LED segments capable of illuminating in green, yellow, or red, each corresponding to a specific range of defect severity levels. The microcontroller aggregates and interprets real-time data collected from various embedded sensors. Based on the cumulative sensor readings and AI-based defect classification, the microcontroller dynamically triggers the appropriate illumination state.

[0045] A green light signifies that the bridge is structurally sound with no significant defects, allowing safe vehicular passage. A yellow light indicates moderate structural anomalies or degradation detected by the system, prompting caution and reduced load advisories. A red light is activated when severe damage, high strain levels, or instability is detected, and automatically restricting access to the bridge via the associated motorized barrier assembly 103.

[0046] In response to the detected defects, the microcontroller actuates an autonomous maintenance unit (AMU) 111 mounted on a guided track 112 laid on the bridge surface, in order to perform the repairing operations autonomously during low traffic intervals to ensure safety and efficiency. The operations of the AMU 111 are fully coordinated by the microcontroller, which relays precise defect location and category data obtained from AI-enabled imaging, LiDAR, ultrasonic, and strain sensors.

[0047] As the AMU 111 starts to maneuver over the track 112, the microcontroller by means of an obstacle detection unit and a GPS module configured with the AMU 111, detect the presence of any obstacle and provides precise geolocation data to support coordinated movements along the guided track 112 to ensures safe and precise navigation across the bridge surface. The obstacle detection unit comprises a set of proximity sensors, ultrasonic detectors, and stereo vision cameras mounted on the AMU 111 body, and configured to scan the surrounding environment for any obstacles such as loose debris, construction materials, pedestrians, or vehicles. These sensors generate real-time spatial data that is processed by the microcontroller to dynamically assess potential collision threats.

[0048] Simultaneously, the GPS module continuously provides location data with high accuracy, allowing the AMU 111 to localize itself on the predefined digital map of the bridge structure 102. The GPS data is cross-referenced with stored track coordinates and operational zones to ensure the AMU 111 remains within the safe bounds of its designated path. If an obstacle is detected within a critical proximity, the microcontroller temporarily halts the AMU’s 111 movement and evaluates an optimal path adjustment based on the severity and position of the obstacle. Depending on the scenario, the AMU 111 may either reroute along a predefined alternate path or wait until the obstruction is cleared. This coordinated functionality of the obstacle detection unit and GPS module allows the AMU 111 to operate autonomously without human intervention while avoiding collisions, ensuring continuous and reliable maintenance performance during low-traffic intervals.

[0049] In case the detected defect corresponds to surface-level fissures or hairline cracks, the microcontroller activates the crack sealing unit 201 of the AMU 111. Upon receiving precise coordinates and crack profiles from the AI-enabled imaging units 107 and LiDAR sensors, the microcontroller actuates a turret arm 202 mounted on the AMU 111, to extend and position an epoxy dispenser 203 integrated with the arm 202, accurately over the crack site. This turret arm 202 is designed with multiple degrees of freedom and is servo-actuated to follow the linear or branched paths of the cracks.

[0050] The turret arm 202 mentioned herein includes a rotary base, servo or stepper motors, linkages or extendable segments, and encoders. The rotary base allows horizontal rotation, while the extendable arm segments are actuated by motors to vary the reach. Encoders provide real-time positional feedback. When the microcontroller receives a positioning command, it activates the motors to rotate and extend the turret arm 202 to the desired angle and length. The integrated epoxy dispenser 203 is then accurately positioned for operation.

[0051] Upon positioning the epoxy dispenser 203, the microcontroller actuates the epoxy dispenser 203 to release a calibrated amount of sealant material. The epoxy is pressure-injected into the crack to ensure deep penetration and complete sealing. The epoxy dispenser 203 consists of an epoxy reservoir, motorized plunger or pump, nozzle, flow control valve, and a pressure regulator. The reservoir stores the epoxy, which is pushed out by the motorized plunger or pump upon actuation. The flow control valve and pressure regulator ensure the resin is dispensed at a consistent rate. The microcontroller governs the operation based on user input or sensor feedback, ensuring precision. The nozzle directs the epoxy over the targeted crack for sealing identified cracks.

[0052] In case the detected defect corresponds to potholes (a hole in the surface of a road that is formed by traffic and bad weather) or depressions on the bridge surface, the microcontroller activates the pothole filling unit 204 of the AMU 111. This unit contains a rotating chamber 205 preloaded with a rapid-curing polymeric or asphalt mix, which is chosen based on environmental conditions and substrate compatibility. The chamber 205 aligns centrally above the pothole and opens a valve to release the filling material in precise volumes for full coverage of the void.

[0053] Upon dispensing of the rapid-curing mix, the microcontroller actuates a compacting roller 206 positioned directly behind the chamber 205, to level and press the mix into the cavity. The roller 206 consists of a cylindrical roller drum, a supporting frame, a motor for actuation, and a pressure-regulating springs or actuators. Once the material is dispensed, the motor rotates the cylindrical roller, which moves across the surface. The pressure-regulating mechanism ensures consistent downward force, enabling uniform compaction. The supporting frame guides the roller’s 206 path, while the microcontroller governs the speed and pressure so that the dispensed material fills cracks evenly and bonds effectively, providing a smooth, durable finish and restoring surface uniformity for long-lasting structural repair.

[0054] In case the detected issue corresponds to surface debris, mud accumulation, or obstruction due to organic material, the microcontroller activates the cleaning unit 207 of the AMU 111. This unit is equipped with multiple swiveling high-pressure water jets 208, which upon actuation, begin to oscillate and sweep across the bridge surface. The direction and pressure intensity of the jets 208 are modulated based on real-time feedback from surface imagery sensors and moisture sensors, ensuring effective cleaning while preventing surface damage. The dislodged debris is either washed toward side drains. This cleaning action not only enhances visual clarity for further inspections but also maintains surface friction and safety for vehicular traffic.

[0055] The swiveling high-pressure water jets 208 are designed to remove debris from surfaces by delivering forceful, targeted water streams. The water jets 208 consist of a high-pressure water pump, swivel nozzles, a motorized rotary actuator, and flexible tubing. Water is pressurized by the pump and delivered to the nozzles via tubing. The motorized rotary actuator enables the nozzles to swivel horizontally and vertically, allowing dynamic coverage over the surface. The microcontroller regulates pressure, direction, and spray duration. As the pressurized jets 208 strike the surface, they dislodge dirt, debris, and loose materials without damaging the underlying structure 102.

[0056] A plank holding container stored worth new planks, is arranged over the AMU 111, and a wooden plank replacement arrangement having a telescopic rod 209, cutting clipper 210, and wrench assembly 211 is installed over the AMU 111. In case the detected defect corresponds to fractured, loose, or deteriorated wooden planks on the bridge deck, the microcontroller activates the wooden plank replacement arrangement to remove and replace the damaged planks with new planks.

[0057] Upon activation, the microcontroller actuates the telescopic rod 209 to extend and engage the cutting clipper 210 with the affected plank, as guided by visual sensors. The extension and retraction of the telescopic rod 209 is powered by a pneumatic unit associated with the device that includes an air compressor, air cylinder, air valves and piston which works in collaboration to aid in extension and retraction of the rod 209.

[0058] The air compressor used herein extract the air from surrounding and increases the pressure of the air by reducing the volume of the air. The air compressor is consisting of two main parts including a motor and a pump. The motor powers the compressor pump which uses the energy from the motor drive to draw in atmospheric air and compress to elevated pressure. The compressed air is then sent through a discharge tube into the cylinder across the valve. The compressed air in the cylinder tends to pushes out the piston to extend. The piston is attached to the rod 209, wherein the extension/retraction of the piston corresponds to the extension/retraction of the rod 209 in order to engage the cutting clipper 210 with the affected plank.

[0059] Upon engagement of the cutting clipper 210, the microcontroller actuates the clipper 210 to sever corroded nails, screws, or damaged portions of the plank. The cutting clipper 210 includes hardened steel or carbide blades, and a motorized actuator. Upon activation, the motor drives the actuator to close the clipper 210 blades with high force. The sharp blades apply concentrated mechanical pressure to cut through metal or wood components.

[0060] Once the damaged planks are detached, the microcontroller engages the wrench assembly 211 to fully unmount the plank. Simultaneously, a fresh plank is retrieved from the plank holding container and transported to the vacant slot using the telescopic rod 209. The rod 209 carefully aligns the new plank and the wrench assembly 211 securely fastens it to the structural frame using preloaded bolts or clips. This ensures restoration of mechanical integrity with minimal manual involvement.

[0061] During operation of the AMU 111, the microcontroller actuates a holographic projection unit 212 installed on the AMU 111 to project real-time informational signs, visual alerts, and directional guidance onto the bridge surface. The projections may include caution symbols, virtual barricades, maintenance zones, estimated time of task completion, and alternate lane indications to inform approaching traffic and pedestrians and enhance operational safety and public communication during maintenance activities.

[0062] The projection unit 212 operates by a combination of light sources, mirrors, and lenses to create a three-dimensional visual representation. The projection unit 212 consists of a laser light source that projects onto a beam splitter, which divides the light into multiple paths. These paths are then directed onto a diffraction grating to produce the holographic image. Micro-lenses and mirrors further focus and align the light to form a clear 3D projection. The microcontroller linked with the projection unit 212 controls the image content, ensuring the correct hologram are depicted for projecting real-time informational signs and visual alerts onto the bridge surface during operation of the AMU 111.

[0063] Further, a weight detection sensor embedded within the bridge surface, and strategically positioned near or at the entry point, to continuously measure the axle load, total vehicle weight, and rate of load application by approaching vehicles. The weight detection sensor used herein is a particular kind of transducer, more especially a weight transducer, which transform a mechanical force that is applied as an input, by the weight of the approaching vehicles, into a change in electrical resistance, which varies proportionally to the force being applied to the sensor. This change in electrical resistance is detected by the microcontroller linked with the sensor, in the form of an electrical signal.

[0064] The microcontroller processes the data in conjunction with existing structural integrity data collected from various monitoring sensors 101 embedded throughout the bridge. Based on predefined load thresholds and the cumulative load already present on the bridge, the microcontroller dynamically determines whether to allow or restrict additional vehicle entry. If the microcontroller detects that the approaching vehicle's weight, when added to the existing live load, exceeds the safety limit defined for the bridge’s current condition, the microcontroller immediately actuates the barrier assembly 103 to prevent entry and simultaneously the multicolor light indicator 108 is switched to red.

[0065] A display unit 109 and a traffic light assembly 110 are installed at the entry point of the bridge, functioning as an integrated vehicle guidance and safety communication. The display unit 109 is a high-visibility, weather-resistant digital screen configured to show real-time advisory messages that include, but are not limited to:

• Current load limits based on bridge capacity.
• Alternate route recommendations in case of access restriction,
• Repair status updates from the Autonomous Maintenance Unit (AMU) 111,
• Real-time structural condition scores, and
• Emergency alerts or notifications during critical structural anomalies.

[0066] The display is directly linked with the microcontroller, which receives continuous input from the embedded structural monitoring sensors 101 and AI-imaging systems. Based on this data, the microcontroller dynamically updates the messages to ensure drivers are informed in advance and make decisions accordingly.

[0067] The traffic light assembly 110, consisting of red, yellow, and green signal lights, which are also controlled by the microcontroller. These lights serve to regulate vehicle entry based on detected structural risks, weight sensor readings, current bridge occupancy/ load, and ongoing repair operations by the AMU 111. For example: Green light allows safe passage, Yellow light warns of approaching load or structural limits, prompting caution, and Red light indicates restricted entry due to excessive load, detected defects, or maintenance activity.

[0068] The present invention works best in the following manner, where multiple interconnected monitoring sensors 101 are embedded throughout the bridge structure 102 for continuous real-time data acquisition regarding structural conditions. Based on assessed structural risk levels the barrier assembly 103 is deployed to regulate vehicle access. The fiber optic strain sensors detect strain levels to assess the need for load restrictions. The network of ultrasonic sensors and accelerometers work collaboratively with ground-penetrating radar (GPR) sensors to detect voids, internal moisture, or abnormal vibrations, the results of which are compiled into defect reports sent to the microcontroller. Further, AI-enabled imaging units 107 and LiDAR sensors capture visual and depth-related information regarding surface anomalies. These inputs are processed using the knowledge distillation-based image processing protocol to classify and prioritize surface defects efficiently. The multicolor light indicator 108 (green, yellow, red) and the traffic light assembly 110 provide real-time visual indications of defect severity and control traffic accordingly, while the adjacent display unit 109 broadcasts advisory messages including load limits, alternate routes, and maintenance updates. Further, the autonomous maintenance unit (AMU) 111 carries out defect resolution and cleaning operations over the bridge.

[0069] Although the field of the invention has been described herein with limited reference to specific embodiments, this description is not meant to be construed in a limiting sense. Various modifications of the disclosed embodiments, as well as alternate embodiments of the invention, will become apparent to persons skilled in the art upon reference to the description of the invention. , Claims:1) A condition monitoring and repair system for constructions, comprising:
i) a plurality of interconnected monitoring sensors 101 is embedded with a bridge structure 102, configured to continuously collect real-time data regarding structural condition of the bridge;
ii) a barrier assembly 103 is positioned at an entry point of said bridge, comprising a vertical rod 104 and a horizontally movable rod 105 attached via a motorized hinge joint 106, wherein said hinge joint 106 is actuated by a microcontroller for controlling entry of vehicles based on structural risk levels determined from said collected data;
iii) a plurality of fiber optic strain sensors is integrated within different layers and joints of said bridge structure 102 for detecting strain levels, wherein said microcontroller processes data from said strain sensors and compares said levels against predefined threshold values to determine necessity for initiating load restriction;
iv) a grid of ultrasonic sensors and accelerometers are installed underneath deck slab and piers of said bridge and integrated with ground-penetrating radar (GPR) sensors, wherein said sensors collaborate to detect abnormal vibrations, voids, or internal moisture, and generate defect reports transmitted to said microcontroller;
v) a set of AI-enabled imaging units 107 and LiDAR (Light Detection and Ranging) sensors mounted along said bridge structure 102 for capturing high-resolution visual and depth information of said surface defects, wherein said microcontroller utilizes a knowledge distillation-based image processing protocol for defect classification and prioritization;
vi) a multicolor light indicator 108 installed at entry point of the bridge, said light indication includes with green, yellow, and red illumination for representing real-time defect severity levels of said bridge based on cumulative sensor readings and AI classification;
vii) a display unit 109 and a traffic light assembly 110 installed at said bridge entry, wherein said display unit 109 is configured to show advisory messages including load limits, alternate routing, and repair statuses, and said traffic light controls vehicle movement based on commands from said microcontroller; and
viii) an autonomous maintenance unit (AMU) 111 mounted on a guided track 112 laid on said bridge surface, wherein said AMU 111 includes:
a) a crack sealing unit 201 with a turret arm 202 and epoxy dispenser 203 for sealing identified cracks,
b) a pothole filling unit 204 with a rotating chamber 205 configured to dispense a rapid-curing mix and a compacting roller 206 for leveling,
c) a cleaning unit 207 with multiple swiveling high-pressure water jets 208 for removing debris from said surface, and
d) a wooden plank replacement arrangement having a telescopic rod 209, cutting clipper 210, and wrench assembly 211 configured to remove and replace said damaged planks with new planks stored in a plank holding container.

2) The system as claimed in claim 1, wherein the microcontroller is coupled with said sensors, AMU 111, traffic assembly 110, and display unit 109, said microcontroller receives and processes said sensor data, and actuates necessary components including said barrier, said AMU 111, said display unit 109, and said traffic lights for autonomous bridge monitoring, maintenance, traffic control, and safety assurance.

3) The system as claimed in claim 1, wherein the monitoring sensors 101 comprises of a surface imagery sensor, depth and profile sensors, moisture sensors, piezoelectric sensors, and temperature sensors.

4) The system as claimed in claim 1, wherein a weight detection sensor is embedded in said bridge surface for detecting and measuring load applied by approaching vehicles, said microcontroller restricts vehicle access based on weight thresholds and cumulative bridge load.

5) The system as claimed in claim 1, wherein said dynamic display unit 109 is configured to display warning messages, rerouting instructions, defect severity scores, and estimated maintenance duration in real-time.

6) The system as claimed in claim 1, wherein said microcontroller actuates said AMU 111 in response to said detected defects, said AMU 111 performs said operations autonomously during low traffic intervals to ensure safety and efficiency.

7) The system as claimed in claim 1, wherein said AMU 111 is further configured with an obstacle detection unit and a GPS module for navigating said bridge surface and avoiding collision during repair operations

8) The system as claimed in claim 1, wherein the knowledge distillation-based image processing protocol enables lightweight real-time inference for surface defect classification while maintaining accuracy similar to heavier convolutional neural networks.

9) The system as claimed in claim 1, wherein a holographic projection unit 212 is installed on said AMU 111 for projecting real-time informational signs and visual alerts onto said bridge surface during operation of said AMU 111.