JP7716114B2 - Safeguarding people and objects from unsafe structures - Google Patents

Description

CROSS-REFERENCE TO RELATED APPLICATIONS
This application claims priority to U.S. Provisional Application No. 16/944,500, filed July 31, 2020, and U.S. Provisional Application No. 15/929,341, filed April 27, 2020, each of which is incorporated by reference in its entirety herein.

This disclosure primarily relates to a system for assessing the structural integrity of a building over time.

Structures of all kinds are designed to withstand forces that they are subject to and are regulated by various government-enforced building codes. These forces include earthquakes, ground vibrations, wind, vibrations from nearby structures/facilities, explosions, landslides, volcanoes, etc. (defined herein as "incidents"). Codes and guidelines are designed to provide a best estimate of whether structural integrity will survive an incident. However, after an incident, even if a building is "code-compliant," an independent ex post determination must be made as to the extent to which the structural integrity may have been compromised and whether the structure is safe for use.

Currently, there are no devices capable of collecting data on a building's structural integrity in real time. While some buildings have been fitted with strong motion instruments, these provide information on the nature of the vibrations that have affected the structure; they do not collect data from the structure to assess the specifics of what has happened to the structure as a result of the vibrations.

Applications have also been developed that provide warnings of approaching earthquake waves. Another type of application provides static analysis of future damage to a structure due to a hypothetical earthquake. None of these types of applications provide detailed determinations of structural damage, nor do they provide an assessment of the safety of the structure as a building.

DISCLOSURE OF THE INVENTION
The present invention provides continuous monitoring of structural attributes before, during, and after an incident. It then uses proprietary algorithms, described further below, to assess the structure's impact status and safety for use as a building. This assessment is delivered to those using the structure within minutes of the incident. Additionally, results from all devices triggered by the event can be aggregated and made available to first responders, government disaster management teams, and critical facility operators.

The present invention uses an array of sensors that continuously monitor the condition of a structure, allowing for highly accurate assessment of the structure's condition over time, particularly immediately after an incident. This allows a person to quickly determine whether the structure is safe for use. The present invention also notifies a person of changes in the structure's condition so that the problem can be resolved and further injury and loss of life can be avoided.

Disclosed is a device for detecting and/or measuring critical components of a structure over a specified period of time by a monitoring unit, defining the specified period as the life of the structure. The device includes the steps of: acquiring, time-stamping, and recording data acquired from each of the sensors in the device; calculating, by an on-board computer device, an average value for each acquired sensor data value by dividing the total of the measured data values by the number of data points in the monitoring sequence; storing the average data value in the on-board memory component; and, when new data acquisition is performed as described above, verifying and comparing the new average value with previous values to determine any variations between data values from the same sensor; categorizing the variations in values using the on-board computer device; and identifying whether any sensor values fall within the predefined default value ranges. (i) If all variations are within the default value ranges, the system stores the values and resets as described above; and (ii) if any variations are outside the default value ranges, triggering a predefined command for further action.

A method for detecting and/or measuring critical changes in a structure using the device includes the steps of: verifying and comparing, by the device's on-board computer, predefined default values specifying minimum and maximum ranges for each sensor with an average default value, wherein the minimum/maximum default values may be defined/modified manually or using machine learning techniques, and such values may be altered/changed at any time; if the average value of any sensor is determined to be outside the default range, a predefined command schedule is initiated; (i) an analysis process is performed to determine conditions (e.g., power and communication capabilities) at the on-board device; (ii) commands are executed based on the results of the analysis; and (iii) data is provided to an external computing device using Wi-Fi, RF, Bluetooth, cellular, satellite, and/or manual extraction from the device for secondary analysis; if the average values of all sensors are determined to be within the default range, the system resets and returns to normal operation.

A method for assessing structural integrity at a remote database location using the device and performing specified actions includes the steps of: receiving analytical data from the device, the analytical data including sensor data obtained from the incident before, during, and after the incident; and performing a secondary analysis, the secondary analysis including (i) data uploaded from the device, (ii) data obtained from a third-party database, and (iii) structural data from a user profile. Relevant data from the structure and its surroundings is identified and calculated by a proprietary algorithm, and the results of the analysis are categorized by a predefined risk assessment table, the predefined risk assessment table having a selected number of actions following the secondary analysis results, and each of the (i) low risk, (ii) medium risk, and (iii) high risk categories is associated with specific guidelines and recommendations, and this information is displayed online in the user profile, delivered to the user via (a) a mobile application, (b) a web application, or (c) TEXT/SMS, or (d) a separate server system, or a combination thereof.

In the above device, the analysis process is performed from a computer unit external to the on-board computing device.

In the above device, the above average value is not calculated as the average value, but is used as the true value from the above sensor data acquisition.

In such devices, the data is not transmitted from the monitoring unit, but is instead processed and analyzed on the local monitoring on-board computing device or an auxiliary computing device in the same location as the object being monitored.

When the data indicates the presence of a body in the room, the device activates the dual communication feature. A person in the room where the monitoring unit is located can use the communication component in the device to communicate with a person at a remote location through the monitoring unit.

In the above device, the monitoring system includes means for billing the user for services provided by the platform.

In the above device, the monitoring system includes means for services provided as a subscription.

On the device, user interactions with the service are accessed via a network provided by the platform and tracked to record user behavior, frequency of use of the service, and types of keystrokes used when using the service, which are compiled to provide feedback and statistics for further software development and vendor advertiser activity.

On the above device, the virtual environment provided by the platform includes a display area for targeted advertising.

In the above device, firmware in individual monitoring units or all monitoring units in the on-board computing device can be updated and commands can be changed at any time.

The device uses micro-controlled acoustic sensors to record sound waves within the structure. These waves are compared to sound patterns generated by the destruction of wood, metal, concrete, glass, stone, and brick to determine structural damage.

In the above device, the individual LiDAR sensors, in cooperation with other sensors, are an essential and necessary part of the invention for measuring distance.

In the above device, the individual gyro sensors, in cooperation with other sensors, are an essential and necessary part of the invention for measuring position.

In the above device, the individual accelerometers, in conjunction with other sensors, are an essential and necessary part of the invention for measuring position and acceleration.

In the above device, the individual temperature sensors, in cooperation with other sensors, are an essential and necessary part of the invention for measuring temperature.

In the above device, the individual smoke sensors, in conjunction with other sensors, are an essential and integral part of the invention for measuring the presence of smoke and fire.

In the above device, the individual passive infrared sensors, in conjunction with other sensors, are an essential and necessary part of the invention for measuring thermal signals from the body.

In the above device, the individual carbon monoxide (CO) sensor, in conjunction with other sensors, is an essential and necessary part of the invention for measuring gases.

Other features and aspects will become apparent from the following detailed description, drawings, and claims.

1 shows an overview of the present invention. The structural database is shown. Indicates device/monitoring unit sensor. Standard recording and data acquisition are shown. 2 shows the upload schedule from the monitoring unit to the data center. Show incident record. Data center analysis is presented. 1 shows multiple monitoring units. The specifications of the monitoring unit are shown below. Illustrates the functionality of the monitoring unit. The analysis process is shown. An example of an algorithm for assessing the impact of an incident on a structure is shown below.

The same reference numerals are used throughout the drawings and detailed description to refer to the same elements. The drawings may not be to scale, and the relative size, proportions, and illustrations of elements in the drawings may be exaggerated for clarity, illustration, and convenience.

The following detailed description is provided to assist the reader in gaining a comprehensive understanding of the methods, products and/or systems described herein. However, various modifications, variations and equivalents of the methods, products and/or systems described herein will be apparent to those skilled in the art.

The present invention is a device [monitoring unit] that uses multiple sensors to record data from measurements such as distance, vibration, sound, heat, gas, light, pressure, humidity, and temperature.

The monitoring unit is attached to a building, bridge, tunnel, or similar structure to identify its structural footprint and record data using sensors such as those shown in Figure 1.

When the system is installed, the user registers the object in the database. Data about the object (i.e., age, size, building materials, building standards) is recorded [construction data]. This construction data is used in the final analysis of the object.

Machine learning techniques are used to identify default values for each sensor's monitored object. Recorded data varies depending on the time of day, season, weather, etc. Machine learning techniques improve the accuracy of these discrepancies in default values.

The sensor records data from the object as shown in Figure 1 and detects fluctuations in that data when the object is subjected to normal or abnormal energy/forces caused by wind, earth movement, explosions or material fatigue [event values] as shown in Figure 10.

Acoustic Emission Technology (AET) is used to detect the failure sounds of different materials. AET defines sound patterns and identifies whether the structure is at risk.

If the object is large, the invention includes an additional monitoring unit that records and processes the same sensor data. Measurement data between the monitoring unit and the slave unit is used as an additional mathematical algorithm in the analysis process. This measurement provides another data set for understanding the object's displacement.

This data is analyzed within the monitoring unit. The results are stored in a database within the monitoring unit and also uploaded to a remote data center for further analysis.

The system checks and compares the event value with the default value.

The system checks and compares with third-party databases [environmental data] (i.e., United States Geological Survey (USGS) and National Weather Service (NWS)). The purpose of obtaining environmental data is to gain a better understanding of the conditions (ground and weather conditions) around the object, and to obtain seismic data from government seismometers in the vicinity of the object.

If available, data from other monitoring units within the same geographic area will also be included in the final analysis of the object.

Final analysis [object data] is categorized using a predefined protocol. A simple categorization for standard users is designed with a color code for determining structural integrity. The following colors are used: green (safe), yellow (needs inspection), red (unsafe). An advanced categorization is designed for experts, with detailed recommendations for determining structural integrity.

Protocols are used for different purposes.

Retrofit/upgrade/repair.

Over time, object data may suggest material fatigue, displacement, or any other type of structural change (indicating that the object may require any type of reinforcement to ensure the structure is safe for use).

Evaluation of the object.

The system can provide a purchaser, real estate broker, bank, or similar public institution with a history of the structural integrity of an object, making this record essential information when appraising an object.

Building permit evaluation.

The system helps improve decision-making by local governments and landowners (i.e., approving building permits, creating new policies and regulations, and any matters related to building codes and safety standards for structures identified in specific geographic locations or objects).

Evaluation of internal structure.

The system assists local governments and owners of internal structures in monitoring and safeguarding larger objects (i.e., gas, oil and water pipelines, power lines, refineries, wind farms, bridges, tunnels, rails) related to geological movements.

Evaluation after natural disasters.

If an object experiences any type of strong seismic motion, the user/owner of the object will receive a notification immediately after such an event regarding the safety standards of the object.

Post-explosion evaluation.

If an object is subjected to a nearby or direct explosion, the user/owner of the object will receive notification immediately after such an event regarding the safety standards of the object.

Post-fracking events and similar man-made events.

If an object undergoes a hydraulic fracturing event and/or similar man-made event, the object's user/owner will receive notification immediately after such event regarding the safety standards of the object.

For each of the seven categories above, proprietary mathematical algorithms are used to automatically determine whether the object has been structurally damaged and/or displaced. Results are delivered to the object's user/owner via SMT/TEXT, email, or phone, as shown in Figure 3. In addition to these methods, results may also be delivered to a separate computer server system, or in combination with these methods.

In certain geographic regions, structures are constantly exposed to vibrations and movements from natural factors (e.g., seismic and weather conditions) as well as nearby man-made internal structures (airports, roads, etc.). This exposure causes building materials to wear down over time (wear and tear). Currently, there is no technology available to determine the state of wear and tear elements in a structure when the structure is constantly exposed to such energies and forces. The wear and tear of materials leads to changes in structural integrity over time, ultimately compromising the safety of the structure. The present invention provides a new layer of data that indicates changes in "wear and tear" that is useful for maintaining the safety of a structure over time.

Currently, no technology exists to determine the condition of a structure as it changes hands or undergoes evaluation. Structures are subjected to daily and/or non-daily forces that wear down building materials over both the long and short term. When structural engineers perform physical inspections, they rely solely on their own eyes to assess whether a structure is safe. These current inspections lack actual or real historical data against which to compare results to determine the condition of the structure. The present invention provides a new layer of data that aids in determining both the value and safety of a structure. Additionally, the present invention provides insight into building materials in ways that are impossible to ascertain through visual inspections.

Currently, if an object is subjected to the forces described above and is located in a populated area, local authorities will immediately close the object for safety reasons until a certified engineer can inspect and classify the structure/object as safe. Such a classification requires a technical team to visit the structure/object. During a geological incident, a large number of structures/objects are often affected. This large number of structures/objects, disproportionately compared to a smaller number of certified engineers, makes the safety classification process time-consuming and cumbersome. The longer it takes, the greater the financial losses to the affected community and the greater the risk of casualties.

The hardware unit used in the present invention monitors whether an object is affected by the incident described above and the amount of such impact. An analytical software system uses data obtained by sensors and converted into mathematical algorithms to automatically determine whether a structure/object is safe. This results in an immediate alert system, increasing public safety and the safety of first responders. The present invention also provides a tool for first responders to improve their understanding of whether it is safe to enter an object after an incident. In addition, it provides local governments with a tool to better and more quickly categorize areas for directing actions to assist people and avoid further injury and casualties. The system categorizes objects after an incident as "most dangerous" to "safest" within minutes.

Individual components or elements that make up the present invention: LiDAR sensor for measuring displacement;
accelerometers to measure ground vibrations;
a piezoelectric device for detecting acoustic emissions;
gas and smoke detectors;
temperature sensor,
a passive infrared sensor that determines the presence of a warm body in the room;
Radio frequency devices for communication between multiple devices connected in a network (RF also measures the distance between monitoring units for displacement detection);
a speaker for playing alarms and pre-recorded sounds (i.e., also allowing for interactive communication);
a cellular communication system with battery backup;
A Wi-Fi system for communication with a remote data center; and/or a gyrocompass to determine rotation.

The components of the device described herein are described below to further define the present invention and its functionality. References to "standard mode" always refer to a mode within the monitoring system that collects data from a structure without any unusual or abnormal forces impacting that structure. References to "incident mode" refer to a situation in which the monitoring system is triggered by one or several factors identified as having impacting values outside of predefined thresholds. These thresholds may be defined during factory configuration of the monitoring system or at the time of installation.

Light detection and ranging sensor [LiDAR]

LiDAR uses a laser to determine the distance between two set points within a structure. Point A is located on the wall where the monitoring unit is mounted, and Point B is located on the other side of the room/space within the structure, opposite the device. In other words, Points A and B can be located inside or outside any object. Data collected between these points is compared with new and historical readings to determine any changes in distance. This data is also used by machine learning techniques to understand variations in distance depending on the time of day, season, weather, etc. This data can also be correlated with other sensor data to further improve data analysis.

Trigger: If the acquired data falls outside of a predefined threshold(s), the LiDAR triggers the system into incident mode. The thresholds vary from object to object and from time to time. The machine learning system continuously adjusts the thresholds as new data becomes available (daily). Temperature sensors, the object's building materials as found in the structural database, the time of day, and the relationship between time periods are all included in determining the distance deviation.

Standard mode: Data acquisition is performed multiple times per day as described in "System mode."

Incident mode: Data acquisition occurs when accelerometer data returns to a value outside the accelerometer threshold.

Accelerometer and gyro

A three-axis accelerometer measures movement, and a gyrocompass is used to determine position. When the monitoring/sensor unit is mounted on a wall within a structure and the system is turned on, a default position is established. The accelerometer monitors and records vibrations 24/7. These records are stored and calculated, and machine learning techniques allow the system to establish accurate default values (in other words, default values). The data is also used to understand distance variations depending on the time of day, season, weather conditions, etc. In addition, the system understands vibrations and rotations. The data is also used to correlate with other data sensors to further improve data analysis.

Trigger: The accelerometer triggers the system into incident mode if the acquired data falls outside a predefined threshold. The threshold varies from object to object and over time. The machine learning system continuously adjusts the threshold as new data becomes available each day.

Standard Mode and Incident Mode: Data acquisition is monitored and recorded continuously as described in "System Mode."

Gas and smoke sensors

The system includes sensors that detect smoke from a fire and various gases (e.g., carbon monoxide (CO), hydrogen cyanide (HCN), carbon dioxide (CO2), and hydrogen chloride (HCl)). These sensors continuously detect gases and immediately trigger the system if a predefined threshold is exceeded. Sensor values are not recorded or stored unless triggered one or more times.

Standard Mode and Incident Mode: These sensors are trigger sensors only.

Temperature sensor

Temperature sensors monitor and record Celsius/Fahrenheit 24/7. These records are stored and cross-referenced with gas sensors to identify the level of explosion risk. These records are cross-referenced with LiDAR and RF to monitor distance calculations. Distance can change due to structural expansion/contraction. In other words, gas lines can be disrupted due to distance changes, increasing the risk of explosion.

Passive infrared sensor (PIR)

When the system enters incident mode, it activates PIR sensors to characterize the presence of a warm body in the room. PIR sensors cannot determine whether the body is a human, an animal, or something else that may be detected as a body-like thermal element. These sensor values are not recorded or stored unless the sensor is triggered one or more times. These sensors are trigger sensors only. If a thermal element is detected by the PIR data, that data is cross-referenced with simultaneously acquired sound data. The system determines whether the combination of the thermal element and sound recording data is due to a human or something else.

Standard and Incident Modes: This sensor is a constant trigger sensor only.

Radio Frequency (RF)

If more than one device needs to be attached, RF is used to triangulate the position and determine the distance between each sensor, which is part of the assessment of object placement and displacement.

Trigger: RF triggers the system into incident mode if acquired data falls outside predefined thresholds. Thresholds vary from object to object and time to time. These thresholds are continuously adjusted by the machine learning system as new data becomes available each day. Temperature sensors, the object's building materials as found in the structural database, time of day and the relationship between time periods are all included in determining the distance deviation.

Standard mode: Data acquisition is performed multiple times per day as described in "System mode."

Incident mode: Triggers data acquisition when the accelerometer returns to a value outside of a predefined accelerometer threshold.

Sound card

The system uses Acoustic Emission Technology [AET] to identify various crash sounds from different materials that make up the structure. A structural database identifies the building materials present, allowing the system to find the crash sounds associated with those specific materials. This AET may or may not be used in the final assessment, depending on privacy and/or other factors.

The relationship between the PIR sensor used to identify people in the room and the sound card can also be used as an interaction parameter. During incident mode, there may be people who are unable to escape the area.

Speaker

The speaker can be used for any kind of warning signal, recorded voice message, or in interactive situations with people in the room.

Wi-Fi

The standard communications used in the system utilizes Wi-Fi. The system uses local Wi-Fi internals for system setup, installation, control, and execution.

Cellular system

The backup system is cellular. In incident mode, if the system experiences a power outage or general power shortage, cellular capabilities are activated to communicate with a remote data center.

Battery backup

If AC/DC power is unavailable, the system will continue to monitor and communicate as designed using its own battery backup system.

If you need to move the unit, you can manually turn off the monitoring unit by selecting offline mode.

According to the present invention, continuous and periodic measurements are taken over a recording interval (typically several minutes long). When all sensors record data values within a predefined range of values [defined normal range], the device performs a calculation for the average value of each sensor, stores the results, and then uploads these values to a data center. This recording sequence is then deleted and a new recording sequence is started without delay or interruption.

If any sensor detects a data value outside of its normal range, (i) the device checks whether AC/DC power is active, and if it is inactive, the backup battery unit is activated; (ii) the device then checks whether Wi-Fi is active, and if it is inactive, another communication component is turned on to communicate with the remote data center; (iii) the recording sequences for all sensors are then uploaded to the remote data center; (iv) local analysis is then performed on the device, and the results are uploaded to the remote data center; (v) the remote data center then performs calculations and obtains data from: (a) the device; (b) a third-party database; and (c) any custom database. The final results based on these calculations are used to determine the status of the monitored object. The results are relayed to the user in the form of: (i) a text message, (ii) an email, (iii) a voice message, or (iv) a customized data set for an external network of computers. In accordance with the present invention, all data is logged, archived, and time-stamped. This process continues until all sensors return to normal range.

The description of the drawings is for the purpose of illustrating selected versions of the invention and is not intended to limit the scope of the invention.

Figure 1 shows an overview of the present invention. Referring to Figure 1, the present invention is a method for determining structural integrity. The present invention comprises: (a) a monitoring hardware system including one or more sensors (Figure 8) for obtaining data from an object being monitored; (b) a software operating system that uses artificial intelligence techniques and logic to verify and compare structural information using an analytical process to understand changes in the values of structural elements; (c) a hosting system located in a remote data center, the hosting system consisting of one or more servers that provide access to online use of the software and provide a platform for the virtual environment, the platform being remotely accessible to multiple simultaneous users via a network; (d) a logic board that includes mathematical operations using algorithms and machine learning techniques (see Figure 12) for processing the analysis; and (e) a communications module that relays messages and recommendations from the analysis to the user.

Figure 2 shows the structure database. Referring to Figure 2, users of the present invention provide contact details and structure data in a registration process prior to using the system. The data is stored in the user database. The database consists of: (i) contact information for owners, managers, users, and any other persons associated with the structure, and (ii) construction information about the structure (e.g., building materials, age, size) (all of which information is an essential part of a structure assessment).

Figure 3 shows the device/monitoring unit sensors. Referring to Figure 3, the present invention includes a hardware monitoring unit using a variety of sensors to identify: acceleration, decibel levels, temperature in degrees Celsius/Fahrenheit, displacement in millimeters, angular rotation, presence of dangerous or toxic gases, smoke, and body heat signals. For structures with large footprints, one or several monitoring units can be used in parallel. Once the monitoring unit is installed on the structure, it connects to the hosting system services in a remote data center via wireless or wired communication through a router over the Internet, satellite, or cellular communication.

The first data acquisition is used to obtain and store the initial default values for the structure. From time to time, software updates can be downloaded and installed remotely from a data center. All or individual monitoring units can be modified for default values and/or data acquisition frequency, recording, and uploading. Figure 4 illustrates standard recording and data acquisition. Sensors in the monitoring units perform daily data acquisition according to a predefined schedule as shown in Figure 4. Standard recording continues nonstop 24 hours a day/7 days a week.

Each sensor implements and calculates the true and current default values of the structure, which are stored in the monitoring unit. Data processing is always performed using machine learning standards, and if adjustments to the default values are necessary, such adjustments are made automatically. In standard monitoring mode, data analysis is performed in the monitoring unit to determine whether a single anomaly exists or whether a relationship-based anomaly between sensors is involved. If all data points are within the default value range, the system continues with the routine standard monitoring schedule.

Figure 5 shows the upload schedule from the monitoring unit to the data center. Referring to Figure 5, results are stored in the monitoring unit and uploaded to the data center. Whenever a force occurs, whether it be vibration, rattle, movement, or any other form of cause, the original state of the structure changes. Such forces are immediately identified. Whenever data from any sensor or combination of sensors in the monitoring unit indicates an incident and/or the data falls outside of default value ranges, the system is activated and goes into incident mode.

Figure 6 shows an incident record. Referring to Figure 6, the system is triggered using a variable that indicates that an incident has occurred.

When an incident occurs, the standard record is replaced by an incident record. A first analysis is performed in the monitoring unit. The resulting analysis and record are uploaded to a remote data center.

Upon receiving a trigger element of the present invention, (i) a command activates the speaker system to play an audible warning and recorded voice message, and (ii) a dialogue feature can be activated to communicate in real time with the person in distress. The incident monitoring schedule continues until the data returns to the default range. During an incident, the present invention obtains recordings using acoustic emission technology capable of identifying fracture sounds within a structure. The incident generates a "ping" that can be used to recognize the failure of different types of materials. Recordings are uploaded to a data center for analysis.

Incident Recording: Recording begins when the system is triggered and continues until the default values are back within range. Incident Process: Regardless of where the standard recording process is in the cycle, when one or more sensors indicate values outside the default value range and the system is triggered, the incident recording process begins and recording continues until the default values are back within range, with additional recording time provided to include post-event data capture. Upload: The entire recording is uploaded to the data center.

Figure 7 illustrates a data center analysis. Referring to Figure 7, the present invention performs a second data analysis at the data center. This analysis includes individual structure data, data from local monitoring units, and third-party databases to understand the types of forces and environmental impacts occurring on the structure. The analysis results trigger predefined recommendations to users of the system. These recommendations are delivered via text message and email immediately after the incident. These recommendations are also constantly displayed online in the user profile. This feature is valuable for anyone wanting to understand the organic evolution (over time and environmental exposure) of a structure.

Figure 8 shows multiple monitoring units. For larger structures, multiple sensor monitoring units may be required. Figure 8 gives an idea of how larger buildings might be monitored.

Figure 9 shows the specifications of the monitoring unit.

The hardware uses eight sensors to obtain data from the monitored objects. The number of sensors is not a limitation of the present invention. Depending on the mounting location of the monitoring unit, more or fewer sensors may be provided. Examples are provided under the heading "Protocols for Different Purposes." These sensors monitor the following:

Accelerometer Acceleration and velocity Gyroscope Direction of movement Laser Distance measurement Microphone Acoustic emission technology Heat Fire Gas Gas Smoke Smoke PIR Passive infrared

The unit contains communications technology, memory, and processing power. The unit is powered by AC/DC with a battery backup system.

The unit includes a speaker system in case a siren or voice message is delivered to the location where the monitoring unit is located.

The present invention is designed to monitor any type of structural object for location and displacement identification. The combination of these components represents a novel electronic monitoring system.

Figure 10 shows the functionality of the monitoring unit.

Data is constantly recorded and fluctuations in the data are detected when the object is exposed to normal energy/forces or abnormal energy/forces caused by wind, earth movement, explosions or material fatigue [event values].

Figure 11 shows the analysis process.

Monitoring unit: Data from each sensor is analyzed. The results of each analysis are compiled into an overall analysis of the object.

Data Center: Data from multiple internal and external databases is compiled and used in the final analysis of the object.

Classification: The above data is classified and associated with preselected messages, which are then delivered to the user/owner of the object.

Figure 12 shows an example algorithm for assessing how a structure has been affected and whether it is safe to occupy and use.

In Figure 12:

1: A monitoring unit attached to a structure that records data after a data acquisition protocol. The data is logged in the system.

2: The logged data and threshold data for each sensor are reviewed and compared.

3: The algorithm determines whether the logged data is within or outside the threshold range.

4: If all sensor values are within the threshold range, the system is reset and no actions/triggers are activated in the algorithm.

5: If one or more sensors are outside their threshold ranges, the system is triggered and enters a validation protocol. The system queries an external database to determine if the structure has been exposed to the incident that triggered the system.

6: If the validation protocol results in a negative result, the system is reset and no further action is required.

7: If the results from the validation protocol are positive, the system analyzes data from the monitoring unit and external databases and categorizes each data set according to a predefined risk classification database.

8: Individual risk classifications are combined into a risk classification report, which includes individual recommendations and warnings.

9: The system delivers this information to the user by risk classification category.

10: Each data acquisition is logged and used by machine learning techniques to generate revised thresholds that are individually customized for the object being monitored. The system calculates a moving average over a predefined number of days (Y) using a simple moving average or a centered moving average (A1 + A2... + Ax) Y days.

When the present invention is used on multiple structures within a geographic area, results can be delivered as a prioritized list of structures sorted by risk class, size, etc. These results can be posted to an official or unofficial database to indicate risk classification to first responders. These results may be made available to those who wish to understand the risk classification of a particular public building (i.e., school, hospital, airport, train station). These results also indicate the location of people who may be in an emergency situation. The present invention can pinpoint the location of people at the time of the incident. This information can be relayed directly to first responders. Results may also trigger requests for action responses in other computerized systems (e.g., power grid systems, nuclear power plants).

The long-term results of standard and incident analyses can be used for: (i) assessment of material fatigue of the structure; (ii) recognition of the occurrence of incidents affecting the structure over time for real estate transactions, insurance underwriting and banking valuation applications or any other type of structural risk assessment; (iii) assessment related to building permits and/or building codes.

On July 4, 2019, a 6.4 magnitude earthquake struck Ridgecrest, California. It was felt as far away as San Francisco. Residents and local governments quickly began dealing with the aftermath of the natural disaster. People returned to their homes, thinking the earthquake was over.

On July 5, 2019, a 7.1 magnitude earthquake struck Ridgecrest, California. The main shock occurred 24 hours after a strong foreshock. The first hit caused significant damage to structures, and the second quake was devastating.

The following week, over 1,000 tremors were recorded in Ridgecrest, California.

This is a typical scenario in which people are at risk because they do not know whether another strong motion event will occur soon, and they do not know whether their homes will be strong enough to survive another earthquake.

The electronic design and layout of the present invention are fabricated on a standard printed circuit board (PCB) based on the Gerber format. All components used are carefully tested before PCB assembly (PCBA). Industry PCBA standards are used in PCB manufacturing. The circuit board is tested and quality controlled, as is the functionality of the individual sensors. The PCB is then assembled into a plastic box made for the product. After assembly, the product is tested for functionality and packaged. Installation and user manuals are included in the package.

Each sensor in the above invention is necessary to facilitate a highly accurate assessment of structural integrity. Machine learning techniques improve performance with each data acquisition from the structure. The more monitoring units attached to a structure and the more sample data acquired to analyze structural behavior, the better risk analysis can be generated.

The analysis may yield the same and/or better results if other types of measurement components for determining location and displacement are used in place of the distance measurement component. The analysis may yield the same and/or better results if other types of acoustic sensors are used in place of the acoustic sensor. The analysis may yield the same and/or better results if the present invention is incorporated into other types of devices (portable or stationary).

The user downloads, installs, and registers the mobile application from the remote data center. Device registration is performed using the mobile application. The user installs the device in a high position on the interior wall of the structure. Additionally, if the user decides to use more than one sensor, at least one sensor must be installed in a low position on the interior wall of the structure. The device initiates a set of mechanical self-checks to verify proper installation. In this invention, once communication with the remote data center is initiated, the remote data center performs a series of functionality tests. Additionally, the user is requested to provide information about the structure (type, age, materials used, etc.) and the names and contact information of relevant parties. At this stage, the device performs its operations without any user interaction.

After an incident, if data records indicate that sensor data has fallen outside of default ranges, the user will receive within minutes an analysis of the impact on the structure resulting from the incident (e.g., building safety recommendations and guidelines). If the analysis concludes that the building is safe, the user may enter the building and resume normal use. If the analysis indicates that significant structural damage has occurred, the user will be advised to remain vacant and seek the assistance of a specialist engineer. In intermediate cases, the analysis may indicate that damage may have occurred. In these situations, the user will be provided with a detailed explanation of what was found, its uncertainties, and suggestions for future action.

Results from all devices triggered by an incident can be aggregated and made available to first responders, allowing them to understand the risk level before entering a structure, and to government disaster management teams, allowing them to begin planning a government response. This information can also be made available to operators of critical internal structures and facilities, allowing for improved management of resources and safety issues after an incident. Additionally, results can be delivered as numerical values (rather than as text messages) into third-party computerized systems. These numerical values can trigger other systems to execute various types of commands.

In addition, data capture conducted before, during, and after an actual natural disaster event can be delivered as a complete data record to insurance companies, local governments, first responders, emergency agencies, etc. to understand the details of what happened to the structure in relation to the event.

The system can also be triggered by third-party databases. Examples of third-party databases include USGS, ASCE, QCN, NWS, NEIC, or any other third-party system, recording events from natural disasters. If any of these third-party databases alert the system to an incident, and the incident falls outside of a threshold range, the monitoring unit can be activated (triggered) (unless already triggered by its own sensor).

Furthermore, when a structure is monitored nonstop over long periods of time, the system detects material fatigue resulting from weak and strong vibrations caused by a wide variety of natural phenomena and other environmental conditions. Over time, the properties of the structure change, and if these changes fall outside threshold ranges, the system is triggered to alert the user.

While specific examples are included in this disclosure, it will be apparent to those skilled in the art upon reading the present disclosure of this application that various changes in form and detail may be made in these examples without departing from the spirit and scope of the claims and their equivalents.

Claims (23)

1. A method for measuring the structural integrity of a structure following a natural disaster affecting said structure, comprising:
acquiring and recording, on one or more occasions each day, data obtained from a LIDAR sensor, a gyro sensor, an accelerometer, a temperature sensor, a smoke sensor, an infrared sensor, and a gas sensor disposed on the structure to measure distance data between two or more points, by a monitoring unit in communication with each of the sensors and including a computing device;
analyzing and calculating, by the computing device, an average value for each of the acquired sensor data by dividing the sum of the measured data values by the number of data points;
storing the average data value in a memory component of the computing device;
ascertaining and comparing the new average value with a previous value to determine, using the computing device, a variance between the new average value and the previous value from the same sensor;
providing a risk assessment decision by categorizing the new average values and variations in the previous values and identifying whether sensor values are within or outside of a predefined default value range, where if all variations in the new average values and the previous values are within the default value range, the system stores the new average values and continues to obtain and record data from the sensors, and if variations are outside the default value range, triggers a predefined command to perform and take further action to determine changes in the structure that affect structural integrity;
comparing the average default value with predefined default values specifying minimum and maximum value ranges for each sensor;
Varying the minimum and maximum value ranges manually and/or using machine learning techniques ;
If the average sensor values are identified as being outside of default value ranges, a predefined command schedule is initiated and an analytical process is performed to determine power and communication capabilities, data is provided to an external computing device via Wi-Fi, RF, Bluetooth, cellular, satellite and/or manual extraction from the computing device for performing secondary analysis to determine the integrity of the structure, and if the average sensor values are identified as being within default value ranges, the system is reset and returns to normal operation . The method of claim 1, wherein the acquiring and recording of data further includes acquiring data from a piezoelectric device sensor. The method of claim 1, wherein the monitoring unit further comprises an audio speaker, a cellular communication system, or a Wi-Fi system. receiving sensor data acquired before, during, and after the natural disaster;
performing an analysis using the data from the sensors, the data obtained from the third party database, and the structure data from the user profile;
analyzing the structure after the natural disaster using a predefined risk assessment table to categorize the structure;
10. The method of claim 1 for measuring structural integrity, further comprising a remote computing device that performs operations including:
The method wherein the predefined risk assessment table has categories of low risk, medium risk, and high risk, and the categories are communicated to a user. The method of claim 1, wherein the analysis process is performed from the computer device and/or from a second computer device outside the structure, or a combination of both. The method of claim 1, wherein true values from sensor data acquisition are used in addition to the average values. The method of claim 1, wherein data is processed and analyzed within the computer device or an auxiliary computing device located in the structure. The method of claim 1, wherein a person within the structure containing the monitoring unit can communicate with a person at a remote location through the monitoring unit using a communication component within the monitoring unit. The method of claim 1, wherein the monitoring unit includes means for billing a user for services provided by the monitoring unit. The method of claim 1, wherein the monitoring unit includes a system for services offered as a subscription. The method of claim 1, wherein user interactions with the monitoring unit are accessed over a network and tracked to record user behavior, frequency of service usage, and types of keystrokes used when using the service, and used for feedback for further software development and vendor advertiser activity. The method of claim 1, wherein the monitoring unit includes a display area for targeted advertising. The method of claim 1, wherein firmware in the monitoring unit or the computing device is updatable, allowing commands to be changed at any time. The method of claim 1, wherein the acquiring and recording of data is further obtained from a micro-controlled acoustic sensor that records sound waves in the structure that are compared to sound patterns generated due to the destruction of wood, metal, concrete, glass, stone, and/or brick for assessment of structural damage. 10. The method of claim 1, further comprising identifying mobile electronic devices in or around structures determined to be unsecure in the analysis process,
A method wherein a user registers the mobile electronic device online and when the system is triggered by an incident, the system recognizes the mobile electronic device when it is in proximity to the monitoring unit. The method of claim 1, wherein the system communicates with a third-party database. The method of claim 1, wherein the system further detects material fatigue. The method of claim 1, wherein the risk assessment includes a low risk assessment, a medium risk assessment, or a high risk assessment, each assessment being accompanied by specific guidelines. The method of claim 1, wherein the monitoring unit further comprises an acoustic sensor. 20. The method of claim 19 , wherein the acoustic sensor identifies human voices to enable notification to people in a room experiencing an earthquake. 20. The method of claim 19 , wherein a combination of data from the acoustic sensor and the infrared sensor identifies whether a body in a room is human or animal. The method of claim 1, wherein the natural disaster is a localized earthquake. The method of claim 1, wherein the gas sensor is a carbon monoxide sensor.