Computer system and computer-readable medium for inferred measurement of construction progress and cost

Abstract

A computing system measures a cost of a construction project. A robotic inspection device remotely inspects the construction project on behalf of a project owner. A computing device receives image data from the robotic inspection device. A project progress measuring module determines a measured progress of the construction project from the image data to identify discrepancies with a project schedule and builder progress report. The project cost measuring module determines from the image data how many workers are working on the construction project. The processor is further configured to execute an artificial intelligence module trained on a corpus of collective bargaining agreements to determine worker wages for the construction project, the project cost measuring module identifying discrepancies between inferred worker wages and contractor-reported worker wages. The system includes an Augmented Reality display device to display an AR overlay of project information indicative of the measured progress of the construction project.

Description

COMPUTER SYSTEM AND COMPUTER-READABLE MEDIUMFOR INFERRED MEASUREMENT OF CONSTRUCTION PROGRESS AND COSTCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority to U.S. Provisional Patent Application 63 / 765,507 filed on February 28, 2025 which is hereby incorporated by reference.TECHNICAL FIELD

[0002] The present invention relates generally to computer-implemented systems, computer-implemented methods and computer-readable media for project management and, more particularly, to systems, methods and computer-readable media for measuring progress and costs of a construction project.BACKGROUND

[0003] Various known project management techniques are routinely employed for managing various types of projects, such as for example large-scale construction projects. Various computer-implemented technologies provide tools for specific aspects of project management have been developed. Some examples are disclosed in the following documents: US10042636, US11321791, US8244565, US20200327467, US20190138961, US20170147960, US20130332368, and US20210110347, all of which are hereby incorporated by reference.

[0004] An issue that frequently arises in the management of construction projects is that the project owner often lacks an effective and efficient way to independently measure the progress of the construction project. When the contractor or builder provides progress reports and updates, it is often challenging for the project owner to independently assess whether the progress reports provided by the builder or contractor accurately reflect the actual progress and costs. Without a tool to measure progress and costs, the project owner is placed in a difficult position to determine if requests from the contractor for payments at certain construction milestones are justified. Similarly, if the contractor claims extra amounts for cost overruns, it is difficult for the project owner to determine if the cost overruns are justifiable without a tool to independently measure progress and costs. Accordingly, there is a need in theconstruction industry for an efficient and effective tool to enable the project owner to independently measure or infer the actual progress and costs of the project.SUMMARY

[0005] Disclosed herein is a system, method and computer-readable medium for independently measuring the progress and cost of a construction project.

[0006] One aspect of the invention is a computing system for measuring a cost of a construction project. The computing system comprises a robotic inspection device for remotely inspecting the construction project on behalf of a project owner. The robotic inspection device has a navigation system for navigating around a periphery of the construction project. The robotic inspection device also has a camera for capturing images of the construction project. The robotic inspection device further has a data transmitter to transmit image data of the images. The system also includes a computing device operable by the project owner and having a data receiver to receive the image data from the robotic inspection device. The computing device comprises a memory for storing a project progress measuring module and a processor cooperating with the memory for executing the project progress measuring module. The project progress measuring module determines a measured progress of the construction project from the image data and compares the measured progress against a project schedule and a builder progress report to identify and present discrepancies between the measured progress and the project schedule and the builder progress report. The processor is configured to execute a project cost measuring module that determines from the image data how many workers are working on the construction project. The processor is further configured to execute an artificial intelligence module trained on a corpus of collective bargaining agreements to determine worker wages for the construction project, the project cost measuring module identifying discrepancies between inferred worker wages and contractor-reported worker wages. The system also includes an Augmented Reality (AR) display device operable by the project owner to view the construction project, the AR display device being communicatively connected to the computing device to receive and display an AR overlay of project information indicative of the measured progress of the construction project.

[0007] Another aspect of the invention is a method for measuring a cost of a construction project. The method entails deploying a robotic inspection device for remotely inspecting the construction project on behalf of a project owner. The robotic inspection device navigates around a periphery of the construction project. The robotic inspection device captures images of the construction project. The robotic inspection device further transmits image data of the images. The method also entails receiving the image data at a computing device from the robotic inspection device. The method further entails determining a measured progress of the construction project from the image data and comparing the measured progress against a project schedule and a builder progress report to identify and present discrepancies between the measured progress and the project schedule and the builder progress report. The method also entails determining from the image data how many workers are working on the construction project. The method also involves using an artificial intelligence module, which is trained on a corpus of collective bargaining agreements, to determine worker wages for the construction project and to identify discrepancies between inferred worker wages and contractor-reported worker wages. The method further includes displaying on an Augmented Reality (AR) display device operable by the project owner the construction project with an AR overlay of project information indicative of the measured progress of the construction project.

[0008] A further aspect of the invention is a non-transitory computer-readable medium comprising computer-readable instructions in software code which when stored in a memory and executed by a processor of a computing device cause the computing device to receive image data collected by a robotic inspection device deployed at a construction project, The code further causes the computing device to determine a measured progress of the construction project from the image data and compare the measured progress against a project schedule and a builder progress report to identify and present discrepancies between the measured progress and the project schedule and the builder progress report. The code also causes the computing device to determine from the image data how many workers are working on the construction project. The code also causes the computing device to execute an artificial intelligence module, which is trained on a corpus of collective bargaining agreements, to determine worker wages for the construction project and to identify discrepancies between inferred worker wages and contractor-reported worker wages.The code further causes the computing device to transmit to an Augmented Reality (AR) display device operable by the project owner an AR overlay of project information indicative of the measured progress of the construction project.

[0009] The foregoing presents a simplified summary of the invention in order to provide a basic understanding of some aspects of the invention. This summary is not an exhaustive overview of the invention. It is not intended to identify essential, key or critical elements of the invention or to delineate the scope of the invention. Its sole purpose is to present some concepts in a simplified form as a prelude to the more detailed description that is discussed later. Other aspects of the invention are described below in relation to the accompanying drawings.BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Further features and advantages of the present technology will become apparent from the following detailed description, taken in combination with the appended drawings, in which:

[0011] FIG. 1 is a schematic illustration of a computer system for independently measuring progress and cost of a construction project in accordance with an embodiment of the invention.

[0012] FIG. 2 is a schematic illustration of a computer system for independently measuring progress and cost of a construction project in accordance with an embodiment of the invention.

[0013] FIG. 3 is a schematic illustration of an Augmented Reality display device presenting an AR overlay on a view of the construction project.

[0014] FIG. 4 is a schematic illustration of the Augmented Reality display device in which the AR overlay displays a predicted completion date and predicted cost for a next phase of the construction project.

[0015] FIG. 5 is a schematic illustration of the Augmented Reality display device in which the AR overlay displays a completion report for a portion of the construction project.

[0016] FIG. 6 is a flowchart depicting a method of measuring progress of a construction project in accordance with an embodiment of the invention.

[0017] It will be noted that throughout the appended drawings, like features are identified by like reference numerals.DETAILED DESCRIPTION

[0018] The following is a description of computer systems and computer-readable media (i.e. software applications) and related computer-implemented methods for independently measuring a cost and progress of a construction project.

[0019] FIGS. 1 and 2 illustrate a computer system for measuring progress and cost of a construction project in accordance with one embodiment of the present invention. The computer system 100 measures progress and cost on behalf of the project owner independently of the builder or contractor. The computer system is generally denoted by reference numeral 100. The computer system 100 is designed to enable a user (e.g. a project owner) to measure or infer progress and cost of a project, in particular a construction project 200. In the example of FIGS. 1 and 2, the construction project 200 is a building such as an office tower or apartment building. This is simply one example of a construction project. The construction project may be, for example, a bridge, tunnel, highway, shopping center, hospital, stadium, arena, factory, subway system, airport, industrial facility, mine, etc.

[0020] As illustrated by way of example in FIGS. 1 and 2, the computer system 100 includes a robotic inspection device 300 for remotely inspecting the construction project 200 on behalf of a project owner 102. The robotic inspection device 300 in one embodiment has a navigation system 310, e.g. a GNSS navigation system, for navigating around a periphery of the construction project. The robotic inspection device 300 has a camera 320 for capturing images, e.g. digital photographs or videos, of the construction project. The robotic inspection device 300 also has a data transmitter 330, e.g. a cellular or satellite transceiver, to transmit image data of the images.

[0021] The computer system 100 includes a computing device 104 for a user 102, e.g. a project owner but could be any other person or entity tasked by the projectowner with independently managing or overseeing the project in whole or in part. The computing device 104 and project owner dashboard 106 enable the user 102, e.g. project owner, to infer or measure progress and cost for the project independently of any reports delivered by the contractor or builder. The project may be a construction project but it may also be any other type of project. The computing device 104 may be a desktop computer, laptop, tablet or mobile device. The computing device 104 includes a user interface, e.g. display, for providing a project owner dashboard 106. The computing device 104 has a data receiver, e.g. modem, router or network interface adapter, to receive the image data from the robotic inspection device. The computing device 104 comprises a memory for storing a project progress measuring module 150 and a processor cooperating with the memory for executing the project progress measuring module 150. The project progress measuring module 150 determines a measured progress of the construction project from the image data and compares the measured progress against a project schedule and a builder progress report to identify and present discrepancies between the measured progress and the project schedule and the builder progress report.

[0022] Alternatively, the computer system 100 includes a server 140 to execute the project progress measuring module 150. In other words, the project progress measuring module 150 may be stored and executed in whole or in part on the server 140 that collaborates with the computing device 104 in a client-server paradigm or in any other distributed computing or cloud computing environment. The computing device 104 may connect to the server 140 via the Internet 116 or other data network, which may involve a virtual private network (VPN). There may be a single server 140, a server cluster, server farm or cloud server. The computing device 104 has a modem, router or data switch to communicate with the server 140 over the Internet 116 using standard Transmission Control Protocol / lnternet Protocol (TCP / IP) datagrams. The server 140 may be a web server using Hypertext Transfer Protocol (HTTP). Web applications may be coded using Hypertext Markup Language (HTML), Cascading Style Sheets (CSS) or JavaScript.

[0023] In the embodiment depicted in FIGS. 1 and 2, the processor is configured to execute the project cost measuring module 150 to determine from the image data how many workers are working on the construction project. This count of activeworkers on the project enables the module 150 to assess progress and cost independently of any reported provided by the contractor or builder. In one embodiment, the project progress measuring module 150 determines resource allocation recommendations to mitigate the discrepancies and presents alternative cost and time scenarios based on the resource allocation recommendations.

[0024] In the embodiment of FIGS. 1 and 2, computing system has an artificial intelligence (Al) module 143 executing on an Al server 141. The Al module 143 has an Al model trained on a corpus of collective bargaining agreements or other union agreements, worker contracts, or other such documents, to determine worker wages for the construction project. The project cost measuring module 150 is configured in one embodiment to identify discrepancies between inferred worker wages and contractor-reported worker wages.

[0025] As shown in FIG. 1, the servers 140, 141 may each have a central processing unit (CPU) 142, a memory 144, a communication interface 146 and an input / output (I / O) device 148. The module 150 may be stored in the memory 144 and executed by the CPU 142 of server 141. The Al module 142 may be stored in the memory 144 and executed by the CPU 142 of server 142.

[0026] In the foregoing description, the artificial intelligence (Al) module 143 develops an Al model that is able to automatically determine, estimate, calculate or infer labour costs for the workers who are working on the construction project. The Al model can be trained to review union agreements and collective bargaining agreements to extract hourly rates and terms using natural language processing (NLP). The following steps may be used to train the Al model.

[0027] In a data collection step, a large dataset of union agreements in the construction industry is collected. Such agreements would cover a wide range of terms, including hourly rates, working conditions, benefits, and any other relevant information.

[0028] In a data preprocessing step, the collected data is cleaned and preprocessed to remove any irrelevant or redundant information. This step removes formatting inconsistencies, standardizes language, and organizes the data into a structured format.

[0029] In a labeling step, the data is labeled (or annotated) by labeling the relevant sections that contain hourly rates and terms. This enables the Al model to identify and extract the desired information accurately.

[0030] Model training is then performed by using machine learning techniques to train the Al model on the annotated data. Techniques such as named entity recognition (NER) and information extraction can be employed to teach the model to identify and extract hourly rates and terms from the union agreements.

[0031] Validation and refinement are then performed. This evaluates the trained model's performance by testing it on a separate validation dataset. This may involve an expert human validation of the results to train the Al model by confirming or correcting the Al model’s learning. An expert human reviews the output of the Al model to determine if the Al model has learned to make correlations correctly.

[0032] In an integration step, once the Al model achieves satisfactory performance, the Al model is integrated into the Al module of the labour management module and / or into the contract tender module. The Al module can then receive new agreements as input, apply the trained model to extract relevant hourly rates and terms, and provide the extracted information at output for use in managing a project and / or in creating contract tenders.

[0033] In a further step of continuous learning and updating, as new union agreements become available, the Al module continues to collect and annotate relevant data to further train and improve the Al model. This ongoing process of continuing learning permits the model to remain up to date with the latest industry agreements and also the Al module to adapt to changes in the industry.

[0034] From the above description, is should be understood that cost and progress measurements can be performed using artificial intelligence or machine learning to perform one or more cost and progress measurement tasks that could not be performed by a human or at least could not be performed efficiently by a human in a practical timeframe to be useful. The system, method or computer-readable medium disclosed in this specification may use various commands, queries, data flows, routines, data objects, data structures, etc., among elements of the software architecture (e.g., modules, network elements, device components, etc.) and datainputs to provide outputs in real time or near real time which are operations and processes that could not be practically performed manually or mentally by a human within the context of the disclosed embodiments.

[0035] The Al module may use artificial neural networks or deep learning technologies can be used in the present embodiments for supervised and unsupervised learning. In supervised learning, models are trained using data that includes examples with inputs and outputs which the Al model learns to predict the outputs given the inputs. In unsupervised learning, no outputs are provided and the model instead learns to derive inferences from the data by itself. The most common type of unsupervised learning is clustering. Deep neural networks (DNNs) can also be used in the embodiments. A DNN may comprise an input layer, hidden layers, output layers, weights, biases, and activation functions. Examples of proprietary and open source deep learning platforms include Tensorflow, CNTK, Torch / Pytorch, and MXNet. DNN models (or in other instances non-DNN models) can use techniques such as, for example, support vector machines, Markov models, linear regression, logistic regression, and decision trees. In some embodiments, the Al module can include or implement perceptrons, multi-layer perceptrons, feedforward neural networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs). In the present embodiments, the Al module trains an Al model using a dataset of training data that enable the Al model to learn a correlation between a project event (e.g. an incorrect start time, incorrect end time, overworking by particular workers, et.) and a project outcome (e.g. time delay, cost overrun, etc). Once the Al model is trained, the Al model can be used by the Al module to automatically monitor progress of a project and to automatically generate alerts if the Al module detects one or more events that have an effect on the project outcome in terms of time and / cost.

[0036] The computing system 100 in the embodiment of FIGS. 1 and 2 includes an Augmented Reality (AR) display device 200 operable by the project owner to view the construction project 200. The AR display device 200 is communicatively connected to the computing device or server to receive and display an AR overlay of project information indicative of the measured progress of the construction project 200. The AR display device 400 may be AR goggles or AR glasses.

[0037] As depicted by way of example in FIG. 1, the memory 144 of the server 141 may also store a database 160 of project management data. This project management data may include collective bargaining agreement (CBA) rates 162, CBA terms 164, burden rates 166, past project metrics 168, historical contractor performance 170, location 172 and weather 174. Other types of data may optionally be included.

[0038] The computing system 100 may optionally obtain data from other data sources to measure or infer cost and progress or to validate or corroborate the inferred or measured progress or cost determined using the techniques described above. For example, the project progress measuring module 150 may be configured to generate and transmit a query to an enterprise resource planning (ERP) server 210 or a project management server 220 to request ERP data 212 or project management data 222. The project progress measuring module 150 may be configured to receive the ERP data 212 or project management data 222 to identify the discrepancies between the measured progress and the project schedule and the builder progress report and to identify the discrepancies between the inferred worker wages and contractor-reported worker wages. Optionally, the project progress measuring module 150 is further configured to generate and transmit a discrepancy report to a builder or contractor to automatically notify the builder or contractor of the discrepancies between the measured progress and the project schedule and the builder progress report and to identify the discrepancies between the inferred worker wages and contractor-reported worker wages.

[0039] The computing system may optionally receive worker shift data reported by one or more mobile devices 112 associated with the one or more workers 110. The mobile device 112 shown by way of example in FIG. 1 includes a microprocessor, memory, battery and a user interface. The user interface may include one or more input / output devices, such as a display screen. The mobile device optionally includes a microphone and a speaker. The mobile device 112 may include forwardly-facing and rearwardly-facing digital cameras. The mobile device includes a cellular radiofrequency (RF) transceiver. The mobile device also includes a locationdetermining subsystem for determining a current location of the user computing device. In the case of a mobile device, the location-determining subsystem may be aGlobal Navigation Satellite System (GNSS) chip such as a Global Positioning System (GPS) chip. The mobile device may also have a Wi-Fi transceiver, a Bluetooth transceiver, a near-field communication (NFC) chip, an accelerometer, and one or more other data communication ports or sockets for wired connections, e.g. USB, HDMI, Lightning connector, FireWire (IEEE 1394), etc. or ports or sockets for receiving non-volatile memory cards, e.g. SD (Secure Digital) card, miniSD card or microSD card. The mobile device 112 shown by way of example in FIG. 1 may be used by each of the workers to input shift report data such as start time, end time, photos of progress, etc. The mobile device 112 may use the radiofrequency transceiver to transmit the shift report data via the base transceiver station 115 through the Internet 116 to the server 140 for processing by the module 150.

[0040] As depicted by way of example in FIG. 2, the robotic inspection device 300 may be an unmanned aerial vehicle (UAV) 300 or a land-based (wheeled or tracked) robotic vehicle 300a. Whether airborne or land-based, the robotic inspection device 300 may optionally include a navigation system (including anticollision) sensors 310, a camera 320 to capture images, an RF transmitter 330, e.g. cellular or satellite transmitter. Optionally, the robotic inspection device 300 may include wall-penetrating radar 340, infrared (IR) camera 350 and thermal imager 360. The robotic inspection device 300 may receive data from other sources like a telemetry module 370 of a work vehicle or construction equipment 371. A wearable sensor 372, e.g. smart watch, on a worker may also communicate data to the robotic inspection device 300. In other words, in one embodiment, the robotic inspection device is configured to receive biometric data from wearable devices worn by workers.

[0041] In one embodiment, the robotic inspection device comprises an RF communication activity sensor to determine an activity level based on RF communication activity, e.g. radio chatter indicative of activity level.

[0042] The robotic inspection device 300 may receive data from an electrical meter 374 that measures electrical power consumption at the construction site. The robotic inspection device 300 may receive data from a perimeter access card reader 376 indicating how many workers have entered the construction site. The robotic inspection device 300 may receive data from a mobile device 112 of a site supervisor or foreman who electronically uploads an electronic record 378 of a safety briefing orother electronic attendance sheet that indicates how many workers are present at the construction project. The robotic inspection device in some embodiments not only captures images of the construction project but can use other sensors to obtain (I R) images, thermal images, radar images, or any combination thereof. These images can be processed and compared to construction plans e.g. CAD drawings of the construction project, or any other drawings, progress charts, etc. to determine a progress status of the construction project. The inferred status can then be compared to a reported status from the builder or contractor. The inferred status can also be obtained from materials delivered or stockpiled on site, waste material leaving the site, construction vehicles on site, movement of construction vehicles, thermal images of construction vehicles, and workers on site.

[0043] In one optional embodiment, the robotic inspection device may have a weather sensor to sense a weather state at the construction project. The data transmitter of the robotic inspection device may transmit the weather state to the computing device to enable the project progress measuring module to estimate a construction delay due to the weather state.

[0044] In one optional embodiment, the robotic inspection device may have a traffic surveillance module determining traffic data affecting material deliveries to the construction project. The data transmitter of the robotic inspection device may transmit the traffic data to the computing device to enable the project progress measuring module to estimate a construction delay based on the traffic data.

[0045] From these various data sources, the system 100 infers construction progress and a construction cost to date based on the workers present and optionally also with regard to materials and equipment on site. The system 100 provides a project owner with an accurate and updated progress assessment and cost incurred to date of the construction project. The system 100 enables the project owner to track construction costs independently of the reports delivered by the builder / contractor. Thus, if the builder / contractor asks for payments or requests extra payment for cost overruns, the project owner can compare inferred progress and inferred cost with the reported progress and report cost. The system 100 thus informs the project owner about any discrepancies between measured or inferred cost and what the builder is reporting or requesting as payment.

[0046] As shown in FIG. 2, and as described above, augmented Reality (AR) glasses or goggles or other AR display device 400 can display progress or status information in an AR view 402 of the construction project 404 when a user is viewing the construction project through the AR glasses. AR glasses can also show real-time locations of workers inside the construction site, which may be reported by location tracking of mobile devices or other sensors carried by the workers. For example, if the contractor reports that the third floor is complete but workers are detected still working on the third floor, then a discrepancy may be declared.

[0047] The AR view can show a current status, a historical status or a future (projected) status. The current status reflects the actual state of the construction project. The historical status shows the state of the construction project at a previous point of time. The future (projected) status shows what the project will look like assuming the project follows the construction schedule.

[0048] As shown for example in FIG. 3, the AR overlay 402 highlights a portion 406 of the AR view of the construction project 404 for which work is to be validated by the project owner. Optionally, the AR overlay presents a computer-generated recommendation or alert 408 to the project owner. The recommendation or alert may give information or guidance to the project owner. For example, it may recommend whether the project owner should validate the work performed on the portion of the construction project that is being highlighted by the AR overlay and / or provide an alert that work is not complete. Optionally, the AR display device displays a prompt to the project owner to validate or invalidate that the work for a highlighted portion of the construction project has been completed. Optionally, the AR display device is configured to display discrepancy data indicative of the discrepancies between the measured progress and the project schedule and the builder progress report. Optionally, the AR display device is configured to display discrepancy data indicative of the discrepancies between the inferred worker wages and the contractor-reported worker wages.

[0049] In the example shown in FIG. 4, the AR display device presents an AR overlay depicting a predicted state of completion 410 of the construction project at a future time specified by the project owner. For example, this may present predicted completion date and a predicted completion cost for a highlighted portion of theproject. Alternatively, as shown in the example of FIG. 5, the AR overlay may present a completion report showing different levels of completion 420 for different aspects of the project (electrical, plumbing, cladding, finishes, etc.)

[0050] The computing system 100 can be used by the project owner using a mobile device to track contractor efforts discreetly in situations where timesheet systems are not in use. This ensures that even without timesheet systems, performance can be recorded and assessed in real-time without the need for contractor knowledge or interaction. This system also enables indirect data collection by incorporating nontimesheet sources such as daily progress reports, payroll data, photographic evidence, site access systems, and manpower-loaded construction schedules to enhance progress measurement accuracy, giving a broader view of work done. Optionally, the system can be integrated with loT for real-time progress tracking. Sensor-based monitoring enables loT devices such as GPS trackers, RFID tags, and biometric scanners to monitor real-time labor and equipment activity, providing more accurate and continuous updates on resource utilization.

[0051] The computing system may be also be used in some implementations for Al-powered forecasting. This leverages historical data and current progress measurements to predict future labor and equipment needs, helping avoid bottlenecks and optimize resource allocation. Additionally, scenario simulations can be performed. This provides "what-if" scenario simulations to assess the impact of resource changes, delays, or schedule adjustments on labor and equipment requirements, enabling proactive management.

[0052] The computing system enables a project owner to track physical progress against labor cost. By developing metrics that measure physical work progress against forecasted labor costs and scheduled progress, the system can ensure projects stay aligned with both time and budget expectations.

[0053] The computing system can also provide comprehensive reporting and dashboards. The system can generate detailed reports and dashboards that offer insights into contractor performance, labor utilization, cost efficiency, and overall project progress. These tools provide real-time updates that stakeholders can rely on for decision-making.

[0054] The system can also provide enhanced collaboration and communication tools. The system may provide a progress communication hub, e.g. a centralized platform for sharing progress updates, photos, and alerts in real-time between field personnel, project managers, and stakeholders, ensuring transparency and alignment across teams. The system can also provide stakeholder dashboards, e.g. tailored dashboards that offer different stakeholders (executives, project managers, etc.) realtime access to relevant project data, helping to ensure that all team members are on the same page.

[0055] The system can only provide benchmarking and best practices library. Industry Benchmarks can includes tools for comparing the project’s performance metrics against industry standards, helping identify inefficiencies and areas for improvement. Best Practices Repository can be a library of best practices and lessons learned from past projects, guiding teams in optimizing performance and avoiding common pitfalls.

[0056] The system can provide alerts and risk warnings, e.g. the system can issue warnings and alerts when observed trends deviate from the awarded contract schedule or cost projections. This helps project teams anticipate potential risks and take early corrective action to avoid delays or cost overruns. The system can also provide risk mitigation and an early warning system that uses risk factor scoring to evaluates multiple risk factors, such as labor productivity, weather, and equipment performance, and generates a project risk score, enabling teams to focus on high-risk areas. Automated early warnings are alerts for deviations in labor productivity, material usage, or schedule slippage to allow early interventions and avoid costly delays or inefficiencies.

[0057] The system can provide automated compliance and contract adherence. Contract Compliance Monitoring can be done by tracking project progress and costs against contractual obligations to ensure adherence to terms and spot early breaches. Automated Reporting for Audits can generate automated reports that show how labor and progress data align with contract specifications, making audits and reviews more streamlined.

[0058] As depicted in FIG. 6, another aspect of the invention is a method 600 for measuring a cost of a construction project. The method entails deploying 601 a robotic inspection device for remotely inspecting the construction project on behalf of a project owner. The robotic inspection device navigates around a periphery of the construction project. The robotic inspection device captures images of the construction project. The robotic inspection device further transmits image data of the images. The method also entails receiving 602 the image data at a computing device from the robotic inspection device. The method further entails determining 603 a measured progress of the construction project from the image data and comparing the measured progress against a project schedule and a builder progress report to identify and present discrepancies between the measured progress and the project schedule and the builder progress report. The method also entails determining 604 from the image data how many workers are working on the construction project. The method also involves using an artificial intelligence module, which is trained on a corpus of collective bargaining agreements, to determine 605 worker wages for the construction project and to identify discrepancies between inferred worker wages and contractor-reported worker wages. The method further includes displaying 606 on an Augmented Reality (AR) display device operable by the project owner the construction project with an AR overlay of project information indicative of the measured progress of the construction project.

[0059] These methods can be implemented in hardware, software, firmware or as any suitable combination thereof. That is, if implemented as software, the computer-readable medium comprises instructions in code which when loaded into memory and executed on a processor of a server or a user computing device such as a desktop, laptop, tablet or mobile device causes the user computing device to perform any of the foregoing method steps. These method steps may be implemented as software, i.e. as coded instructions stored on a computer readable medium which performs the foregoing steps when the computer readable medium is loaded into memory and executed by the microprocessor of the computing device. A computer readable medium can be any means that contain, store, communicate, propagate or transport the program for use by or in connection with the instruction execution system, apparatus or device. The computer-readable medium may be electronic, magnetic, optical, electromagnetic, infrared or any semiconductor system or device. Forexample, computer executable code to perform the methods disclosed herein may be tangibly recorded on a computer-readable medium including, but not limited to, a floppy-disk, a CD-ROM, a DVD, RAM, ROM, EPROM, Flash Memory or any suitable memory card, etc. The method may also be implemented in hardware. A hardware implementation might employ discrete logic circuits having logic gates for implementing logic functions on data signals, an application-specific integrated circuit (ASIC) having appropriate combinational logic gates, a programmable gate array (PGA), a field programmable gate array (FPGA), etc. For the purposes of this specification, the expression “module” is used expansively to mean any software, hardware, firmware, or combination thereof that performs a particular task, operation, function or a plurality of related tasks, operations or functions. When used in the context of software, the module may be a complete (standalone) piece of software, a software component, or a part of software having one or more routines or a subset of code that performs a discrete task, operation or function or a plurality or related tasks, operations or functions. Software modules have program code (machine-readable code) that may be stored in one or more memories on one or more discrete computing devices. The software modules may be executed by the same processor or by discrete processors of the same or different computing devices.

[0060] For the purposes of interpreting this specification, when referring to elements of various embodiments of the present invention, the articles “a”, “an”, “the” and “said” are intended to mean that there are one or more of the elements. The terms “comprising”, “including”, “having”, “entailing” and “involving”, and verb tense variants thereof, are intended to be inclusive and open-ended by which it is meant that there may be additional elements other than the listed elements.

[0061] This invention has been described in terms of specific implementations and configurations which are intended to be exemplary only. Persons of ordinary skill in the art will appreciate that many obvious variations, refinements and modifications may be made without departing from the inventive concepts presented in this application. The scope of the exclusive right sought by the Applicant(s) is therefore intended to be limited solely by the appended claims.

Claims

CLAIMS:

1. A computing system for measuring a cost of a construction project, the computing system comprising:a robotic inspection device for remotely inspecting the construction project on behalf of a project owner, the robotic inspection device having a navigation system for navigating around a periphery of the construction project, the robotic inspection device having a camera for capturing images of the construction project, the robotic inspection device having a data transmitter to transmit image data of the images;a computing device operable by the project owner and having a data receiver to receive the image data from the robotic inspection device, wherein the computing device comprises:a memory for storing a project progress measuring module;a processor cooperating with the memory for executing the project progress measuring module, wherein the project progress measuring module determines a measured progress of the construction project from the image data and compares the measured progress against a project schedule and a builder progress report to identify and present discrepancies between the measured progress and the project schedule and the builder progress report;wherein the processor is configured to execute a project cost measuring module that determines from the image data how many workers are working on the construction project;wherein the processor is further configured to execute an artificial intelligence module trained on a corpus of collective bargaining agreements to determine worker wages for the construction project, the project cost measuring module identifying discrepancies between inferred worker wages and contractor-reported worker wages;an Augmented Reality (AR) display device operable by the project owner to view the construction project, the AR display device being communicatively connected to the computing device to receive anddisplay an AR overlay of project information indicative of the measured progress of the construction project.

2. The computing system of claim 1 wherein the AR overlay highlights a portion of the construction project for which work is to be validated by the project owner.

3. The computing system of claim 2 wherein the project progress measuring module determines and presents a computer-generated recommendation to the project owner to recommend whether the project owner should validate the work performed on the portion of the construction project that is being highlighted by the AR overlay.

4. The computing system of claim 3 wherein the AR display device displays a prompt to the project owner to validate or invalidate that the work for a highlighted portion of the construction project has been completed.

5. The computing system of claim 1 wherein the AR display device is configured to display discrepancy data indicative of the discrepancies between the measured progress and the project schedule and the builder progress report.

6. The computing system of claim 1 wherein the AR display device is configured to display discrepancy data indicative of the discrepancies between the inferred worker wages and the contractor-reported worker wages.

7. The computing system of claim 1 wherein the project progress measuring module determines resource allocation recommendations to mitigate the discrepancies and presents alternative cost and time scenarios based on the resource allocation recommendations.

8. The computing system of claim 1 wherein the AR display device presents an AR overlay depicting a predicted state of completion of the construction project at a future time specified by the project owner.

9. The computing system of claim 1 wherein the robotic inspection device comprises a wall-penetrating radar to obtain radar data and wherein theproject progress measuring module determines the measured progress of the construction project from both the image data and the radar data.

10. The computing system of claim 1 wherein the robotic inspection device comprises an RF communication activity sensor to determine an activity level based on RF communication activity.

11. The computing system of claim 1 wherein the robotic inspection device receives biometric data from wearable devices worn by workers.

12. The computing system of claim 1 wherein the robotic inspection device has a wireless data interface to wirelessly obtain a telemetry data from work vehicles and construction equipment working on the construction project, wherein the project progress measuring module determines the measured progress of the construction project from both the image data and telemetry data.

13. The computing system of claim 1 wherein the robotic inspection device has a wireless data interface to wirelessly obtain a power consumption reading from an electrical meter that measures electrical power consumed at the construction project, wherein the project progress measuring module determines the measured progress of the construction project from both the image data and the power consumption reading.

14. The computing system of claim 1 wherein the robotic inspection device is an unmanned aerial vehicle (UAV).

15. The computing system of claim 1 wherein the robotic inspection device comprises a weather sensor to sense a weather state at the construction project, wherein the data transmitter of the robotic inspection device transmits the weather state to the computing device to enable the project progress measuring module to estimate a construction delay due to the weather state.

16. The computing system of claim 1 wherein the robotic inspection device comprises a traffic surveillance module determining traffic data affecting material deliveries to the construction project, wherein the data transmitter ofthe robotic inspection device transmitting the traffic data to the computing device to enable the project progress measuring module to estimate a construction delay based on the traffic data.

17. The computing system of claim 1 wherein the computing device receives worker attendance data from a perimeter access card reader that reads access cards of workers entering the construction project.

18. The computing system of claim 1 wherein the computing device receives attendance data from a daily safety briefing.

19. The computing system of claim 1 wherein the project progress measuring module is configured to generate and transmit a query to an enterprise resource planning (ERP) server or a project management server to request ERP data or project management data, wherein the project progress measuring module is configured to receive the ERP data or project management data to identify the discrepancies between the measured progress and the project schedule and the builder progress report and to identify the discrepancies between the inferred worker wages and contractor- reported worker wages.

20. The computing system of claim 19 wherein the project progress measuring module is further configured to generate and transmit a discrepancy report to a builder or contractor to automatically notify the builder or contractor of the discrepancies between the measured progress and the project schedule and the builder progress report and to identify the discrepancies between the inferred worker wages and contractor-reported worker wages.

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