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City of Diamond Bar Proposal_TEE
STATEMENT OF QUALIFICATIONS PAVEMENT MANAGEMENT PROGRAM (PMP) UPDATE CITY OF DIAMOND BAR DECEMBER 20, 2024 SUBMITTED TO: City of Diamond Bar Christian Malpica -Associate Engineer Department of Public Works Malpica@DiamondBarCA.gov SUBMITTED BY: Tiger Eye Engineering (TEE) Hamed Majidifard, PhD, EIT Co -Founder info@tigereye-eng.com TABLE OF CONTENTS 1 Cover Letter .................................................... 3 2 Staffing Qualifications ................................... 4 3 Relevant Experience ...................................... 9 4 Project Schedule and Proposed Field Data Collection and Analysis Methods .................11 link low, \1 5 Appendix (Sample of Consultants Quality of Work, Certificate of Insurance) ................... 20 December 20, 2024 Attention: Christian Malpica, Associate Engineer City of Diamond Bar, Department of Public Works Malpica@DiamondBarCA.gov Subject: Request for Proposals (RFP) for Pavement Management Program (PMP) Update Dear Christian Malpica and Selection Committee, Tiger Eye Engineering (TEE) has reviewed the Request for Proposals, and we are pleased to present this package to the Diamond Bar in collaboration with our subcontractors Atlas Technical Consultants and AtlasView to provide our exceptional services for the Pavement Management Program (PMP) Update. TEE is believed to be the world's first pavement engineering company devoted to providing cutting -edge machine -learning based software for rapid, bias -free pavement evaluations coupled with an integrated visualization platform. Recently established in 2021 in collaboration with the University of Missouri -Columbia, its founders have over 100 years of collective experience in pavement engineering and transportation data science and a robust portfolio of pavement condition assessment projects and proprietary software programs. TEE's business model involves partnering with local engineering firms to provide clients with high -quality, locally validated pavement evaluations at a highly competitive price point. This is made possible by its powerful and efficient machine -learning based software programs. TEE's software licensing partnership with the University of Missouri -Columbia keeps TEE at the cutting edge of pavement data science through convenient and continuous transfer of technology developed at Mizzou by TEE's founding members to the pavement engineering community through Tiger Eye Engineering, LLC. TEE has the capabilities and resources to perform all services required during this contract. We are adept at providing services on short notice and in a timely manner to meet critical deadlines and schedules. We will deliver a commitment to the City to focus on safety, communication, cost, schedule, and quality. We appreciate the opportunity to submit this proposal to the City and we are confident that we can provide these services to you in the timely and cost-effective manner that you expect from Atlas. Should you have any questions or require additional information, you can contact Hamed Majidifard, PhD, EIT, TEE's Project Manager, via the contact information listed above. Tiger Eye Engineering acknowledges and accepts the Professional Service Agreement issued by the City of Diamond Bar in relation to this Statement of Qualifications. Hamed Majidifard, PhD, EIT Co -Founder - Tiger Eye Engineering (573) 424-3926 info@tigereye-eng.com TIGER EyE 3 STAFFING QUALIFICATIONS CONTRACT MANAGER *William Buttlar, PhD, PE, SME, Professor DISTRESS EXTRACTION COMPILATION & REPORTING Farzan Kazemi, PhD, Task Lead Amir Ghanbari, PhD Street Saver Update and Implementation Brett Haggerty, PE Principal Engineer/ Pavement Distress Identification Expert A Liya Jiao, PhD, PE Baron Colbert, PhD Flexible (Asphalt) & Composite Pavement Features Kamran Amini, PhD, 19 Meysam Najimi, PhD, PE Rigid (Concrete) Paveme Features *Yaw Adu-Gyamfi, Ph GIS & Data Analyti� Manager TRAINING SERVICES *Hamad Majidifard, PhD, EIT *Farzan Kazemi, PhD, PE PROJECT MANAGER *Hamed Majidifard, PhD, EIT EXISTING PMS DATA REVIEW & NEW DATA COLLECTION *Hamed Majidifard, PhD, Task Lead Baron Colbert, PhD Senior Engineer/ Strategic Planning *Farzan Kazemi, PhD, P QA/QC Planning Liya Jiao, PhD, PE Raul Chavez, MS Staff Engineers/ Feature Extraction Staff SUPPORT SERVICES Jessica Forbes, EIT Robert Carter, EIT Dispachers STAFF MEMBER QUALITY MANAGER *Farzan Kazemi, PhD, PE PAVEMENT TREATMENT PROGRAM *William Buttlar, PhD, PE, SME, Professor 16 Task Lead Joe Fiello, PE Pavement Treatment Expert *Farzan Kazemi, PhD, PE Report Development Liya Jiao, PhD, PE Staff Engineers CONSTRUCTION MANAGEMENT SERVICES *Reza Saeedzadeh, PhD, PE, GE Task Lead *Nickey Akbariyeh, PE Construction Management Expert TEE has assembled a highly experienced team of experts ready to meet the needs of the City of Diamond Bar. HAMED MAJIDIFARD, PHD, EIT PROJECT MANAGER OAKLAND, CA TIGER EYE zjalwo Dr. Majidifard will serve as the project manager for the City. Dr. Majidifard's project experience includes the development and deployment of machine learning techniques to evaluate pavement and infrastructure condition, rating, and roughness. He has a specialized focus on promoting sustainability and durability in pavements, including balanced mix design, crack resistance, and the innovative use of modern recycled materials. Dr. Majidifard has undertaken several projects to develop programming approaches that utilize machine learning techniques to forecast low and high -temperature asphalt mixture performance properties. He has also applied deep learning techniques to create a highly automated pavement evaluation and monitoring system, following ASTM procedures. A full software suite has also been developed, which allows seamless integration of collected data and condition ratings into visualization software and client -specific GIS platforms. PROFESSIONAL EXPERIENCE CITY OF THORNTON, CO - AUTOMATED PAVEMENT INSPECTION AND DATA VISUALIZATION JEFFERSON CITY, MO - PAVEMENT CONDITION ASSESSMENT AND MANAGEMENT SYSTEM PEORIA, IL - PAVEMENT CONDITION ASSESSMENT WASHINGTON, IL - PAVEMENT CONDITION ASSESSMENT INSPECTION AND DATA VISUALIZATION BILL BUTTLAR, PHI), PE °NEWOM CONTRACT MANAGER, SUBJECT MATTER EXPERT, CO-FOUNDER TIGER EYE z1a,wo COLUMBIA, MO Dr. Buttlar is the managing partner of Tiger Eye Engineering and a Professor of Civil Environmental Engineering at the University of Missouri -Columbia, where he holds the Glen Barton Endowed Faculty Chair in Flexible Pavements. He is also the Founding Director of the Missouri Center for Transportation Innovation and is Editor -at -Large of the International Journal of Road Materials and Pavement Design. Dr. Buttlar specializes in pavement evaluation and design, modeling and machine learning, material characterization, pavement management systems, and pavement sustainability and resilience. Dr. Buttlar has served as the project manager/technical lead for many pavement evaluation, design and management projects for various state highway agencies, tollways, and cities across the United States over the past three decades. His responsibilities for those projects included supervising the technical aspects of the projects, managing budgets, assuring timely deliverables, managing personnel and spearheading team communications and technology transfer. PROFESSIONAL EXPERIENCE CITY OF THORNTON, CO - AUTOMATED PAVEMENT INSPECTION AND DATA VISUALIZATION JEFFERSON CITY, MO - PAVEMENT CONDITION ASSESSMENT AND MANAGEMENT SYSTEM 5 KANSAS CITY, MO - REVIEW OF PAVEMENT INSPECTION AND DATA VISUALIZATION YAW ADU-GYAMFI, PHD GIS & DATA ANALYTICS MANAGER COLUMBIA, MO rrver4 �Y� Dr. Adu-Gyamfi is an assistant professor in the Civil and Environmental Engineering Department at the University of Missouri - Columbia and a managing partner of Tiger Eye Engineering. Dr. Adu-Gyamfi is a national expert in the application of Al for highway transportation. He is one of 30 members selected by the National Academies' Transportation Research Board to serve as a member of the standing committee on Artificial Intelligence. He is also the recipient of the prestigious NSF Career grand for advancing machine learning and big data analytics methods in transportation. Dr. Adu-Gyamfi has served as PI or co -PI on more than $5 million funder projects for developing innovative technologies involving sensor fusion, computer vision, machine learning and Big Data analytics. He has a successful track record in developing cutting edge data base solutions, and data products geared towards improving transportation systems management and operations. PROFESSIONAL EXPERIENCE CITY OF THORNTON, CO - AUTOMATED PAVEMENT INSPECTION AND DATA VISUALIZATION JEFFERSON CITY, MO - PAVEMENT CONDITION ASSESSMENT AND MANAGEMENT SYSTEM KANSAS CITY, MO - REVIEW OF PAVEMENT INSPECTION AND DATA VISUALIZATION AMIR GHANBARI, PHD, PE SENIOR TRANSPORTATION ENGINEER COLUMBIA, MO TIGER EYE Dr. Ghanbari, PhD, EIT is a dedicated project manager/engineer with a strong background in pavement management, pavement materials, highway and airport pavement design, and construction supported by ten years of work experience. Dr. Ghanbari has extensive experience with pavement management systems, non- destructive testing and falling weight deflectometer back calculations, pavement condition index analysis, pavement life cycle cost analysis and preservation techniques, highway and airport pavement design, and asphalt material characterization. He has also contributed to several FHWA reports and 20 published articles. Dr. Ghanbari is proficient with various types of pavement/asset management software such as AgileAssets, Paver, StreetSaver, Lucity, and Cartegraph. rKOFESSIONAL tXPERIENCE CITY OF DALLAS, TX - PAVEMEMNT CONDITION SURVEY, SIDEWALK INVENTORY, AND SELECT PROJECT -LEVEL GPR TESTING PROJECTS CITY OF CENTENNIAL, CO - IMPLEMENTATION OF PAVEMENT MANAGEMENT SYSTEM ROUTT COUNTY, CO - IMPLEMENTATION OF PAVEMENT MANAGEMET SYSTEM 0 FARZAN KAZEMI, PHD, PE SENIOR ENGINEER SACRAMENTO, CA �r , Ow 1L= I IL T� Dr. Kazemi is a materials and pavement expert with 10 years of experience in the industry. His expertise includes material testing and characterization, asphalt mix and binder testing, viscoelastic modeling, destructive & non-destructive pavement testing, and friction and smoothness testing. He has been involved in several Federal Highway Administration (FHWA) studies and has co-authored FHWA reports, as well as projects for other agencies, such as the Department of Defense (DoD) and several Departments of Transportation (DOTs), including Caltrans, Nevada DOT, New Jersey DOT, Delaware DOT, Virginia DOT, Maryland DOT, Pennsylvania DOT, and New York State DOT. Dr. Kazemi's work with Caltrans includes leading and supporting projects involving the development of materials specifications, as well as evaluating advanced equipment for material surface testing under the Pavement and Materials Partnering Committee (PMPC) and METS Technical Committees. PROFESSIONAL EXPERIENCE PROFESSIONAL AND TECHNICAL SPECIALIST ENGINEERING SERVICES (CONTRACT 59A1264), CALIFORNIA DEPARTMENT OF TRANSPORTATION PROFESSIONAL AND TECHNICAL SPECIALIST ENGINEERING SERVICES FOR METS, CALIFORNIA DEPARTMENT OF TRANSPORTATION (CONTRACT 59A1115) VARIOUS PROJECTS, ADVANCED INFASTRUCTURE DESIGN, HAMILTON NJ REZA SAEEDZADEH, PHD, PE, GE Mum PROJECT ENGINEER/QUALITY ASSURANCE �r 1"T SAN DIEGO, CA Reza obtained a doctorate in geotechnical engineering from the University of Texas. His ten years of industry experience includes performing and supervising subsurface soil explorations, in situ testing to assess high-speed rail embankment construction, pavement condition assessment of roadways and airfields, field observation of ground improvements, micropile foundations, shored excavations, assignment and review of laboratory testing programs. Reza is proficient in the analysis and design of a wide range of geotechnical and pavement engineering problems, such as stability of slopes, liquefaction potential, buried pipelines, and shallow and deep foundations. PROFESSIONAL EXPERIENCE SOUTH PALM CANYON DRIVE BRIDGE REPLACEMENT AT TAHQUITZ CREEK CHANNEL, FEDERAL AID PROJECT NO. BRLS-5282(042) INTERSTATE 605 AND STATE ROUTE 91 INTERCHANGE PROJECT, LOS ANGELES COUNTY METROPOLITAN TRANSPORTATION AUTHORITY (LA METRO) INTERSECTION IMPROVEMENTS ON HYPERION AVENUE AND GLENDALE BOULEVARD, CITY OF LOS ANGELES 7 NICKEY AKBARIYEH, PE PROJECT ENGINEER SAN DIEGO, CA _ter 1T �1 Nickey has four years of experience in heavy civil and construction aspects of gas and electrical power plants in Iran. Her responsibilities included assisting in the design of the pipelines, foundations, and structures. She also performed design and code compliance reviews, construction management, site visits, the preparation of final as -built reports, and proposal and cost estimating. Her graduate degree in Civil Engineering focused on Pavement and Geotechnical engineering. She has ten years of experience performing and managing subsurface investigations, analysis, laboratory testing, and design report preparations. She also has extensive experience in managing the geotechnical aspects of heavy civil, commercial, and residential construction projects, material testing, and special inspection services for the sites located throughout northern and southern California. Currently, Nickey is a Project Engineer for several high -profile geotechnical and construction project sites in Los Angeles, Orange, and San Diego Counties. PROFESSIONAL EXPERIENCE AIRPORT FACILITY RENOVATION, FULLERTON MUNICIPAL AIRPORT POLICE DEPARTMENT AIR SUPPORT UNIT HANGER, NORMAN Y. MINETA SAN JOSE INTERNATIONAL AIRPORT CITY OF MOUNTAIN VIEW SEWER LINE REPLACEMENT, PROJECTS 18-21 AND 18-22, DEPARTMENT OF PUBLIC WORKS M City of ralesburg, IL ITEE provided Pavement 13 months I$47,000 Network Size: 120 Miles Evaluation, Sidewalk ' Evaluation, Curb & Gutter, and Asset Inventory services to the City of Galesbgurg. Aaron Gavin agavin@ci.galesburg.il.us Citv of Washington, IL TEE provided Pavement 2 months $22,000 Network Size: 115 Miles Evaluation, Sidewalk Evaluation, Curb & Gutter, and Asset Inventory services to he City of Washington. Dennis Carr dcarr@ci.washington.il.us City of Mexico, MO TEE provided Pavement �2 months $15,000 Network Size: 110 Miles Evaluation, Sidewalk Evaluation, and Curb & Gutter services to the City of Mexico. Drew Williford ailiford@mexicomissouri.org Jefferson Citv_ MO TEE provided Pavement 6 months $67,000 Network Size: 245 Miles Evaluation and Curb & Gutter Condition Evaluation Britt Smith bsmith@jeffersoncitymo.gov s amity or veoria, IL Network Size: 100 Miles Lucinda (Cindy) Loos cloos@peoriagov.org (309) 713-1402 City of Olathe_ Ke Network Size: 80 Miles Anthony Golec agolec@oletheks.org ity of Topeka, KS etwork Size: 700 Miles Wacker er@jeo.com city of I norton, cu Network Size: 420 Miles ictoria Simonds ictoria.simonds@thorntonco. ov ity of Guelph, Ontario, anada etwork Size: 550 Miles ichael Navarra navarra@thurber.ca Cape Brenton, Nova Scotia, Canada Network Size: 700 Miles Kelsey Green kelsey.green@eagle-eac.com =E provided Pavement, alk, and Asset evaluatioi e City of Peoria. TEE provided Pavement Evaluation of Trails for the of Olathe. TEE provided Sidewalk Evaluation (MPO) Service the City of Topeka. TEE provided Pavement Evaluation and Pavement Management Program se to the City of Thornon. TEE provided Pavement Evaluation Services for th of Guelph. TEE provided Pavement Evaluation Services for C Brenton, Nova Scotia, Ce 10 Tasks 4/2025 5/2025 6/2025 7/2025 8/2025 Task 1: Agreement for Professional Services Task 2: Kick-off/Progress Meetings 7 Task 3: Inventory, Inspection, and Evaluation of Existing Routes Task 4: Resource Requirements and Fee Schedule Task 5: Pavement Condition Inspection and Evaluation Task 6: Pavement Management Program Report Task 7: Training Task 8: Presentation Pavement Inspection/Evaluation Services Pavement condition assessment provides critically needed information allowing owner -agencies to make more cost-effective and consistent decisions as they manage their network of urban and/or rural pavements. Generally, pavement distress inspections are performed using sophisticated data collection vehicles and/or foot -on -ground surveys. In either approach, the process of distress detection is sub -optimal, as it inherently contains human bias, is very costly and inefficient, and can introduce on -site inspector safety risks. The Tiger Eye Engineering (TEE) automated pavement evaluation software suite was developed by coding and integrating several machine learning and deep learning techniques for distress detection and pavement condition assessment (Figure 1). 11 STEP 2 Training and prediction ca o# STEP Data collection STEP 4 STEP 6 PC1 Strategic management plan V 0% STEP 3 STEP 5 Type Extent Severity Map visualization Artificial Intelligence ,,/ & Deep Learning Figure 1. Application of Al to Detect Type, Extent and Severity of Distresses In Figures 2 and 3, samples of detections from 28 distinct distress types on both flexible and rigid pavement are displayed. The Al models have annotated the images, based on extensive model training conducted over the past 5 years using more than 50,000 expert -annotated images. The annotations (detections) indicate both the type and extent of each distress, and a second ML-based model can detect the severity of each distress. Figure 2. Example of Flexible Pavement Distresses Detected in Jefferson City, MO 12 Block Cracking Severe Fatigue Cracking Multiple Potholes PCI: 60 PCI: 35 PCI: 15 Fair Very Poor Failed Figure 5. Distress Detection vs PCI We will also provide, free of charge, our rapid International Roughness Index (IRI) evaluation for the network, using our machine learning based approach combined with measured acceleration and GPS data. Our software suite includes a series of custom-built modules, including the following: 1. Driving/routing software - allows our drivers to efficiently navigate the roads to be surveyed (can be shared with the owner -agency). 2. Cloud -based annotation tool - allows us to collaborate across the team and with owner -agencies on training the machine learning models for accurate and custom distress or asset detections and ratings. 3. Annotated image libraU - containing hundreds of thousands of trained/annotated images, trained by leading pavement engineers. 4. Al algorithm.- analyzes the captured videos, makes distress detections, and superimposes the labeled distresses onto images (trained with deep machine learning including convoluted neural networks using the annotated dataset). 5. Post -processing software algorithms - automates the process systematically and consistently handling overlapping detections, and produces industry -standard ratings (PCI, CRS, PASER), and/or user -defined ratings (e.g., OCI). 6. Quality assurance —As described in a later section, TEE uses a rigorous, statistics -based approach for QC and QA operations. All work is done in-house, in the US, by our team of pavement experts and in collaboration with our clients. We never outsource our work. 7. Data visualization platforms - allow users to make sense of millions of distress detections over thousands of road sections through heat maps and real-time data analytics and statistics. Standard and user - selectable filters are also provided on the interface. The platform also allows convenient access to videos, pictures from camera feeds (HD, 360), and annotated pictures showing distress determinations, including their type, extent, and severity. The client will have access to the dashboard and images throughout the duration of the project. In the future, a convenient software -as -a -service (SaaS) arrangement can be established to provide the client with continually updated pavement assessment data and asset management integration. 8. Data import/export systems - allowing quick and seamless integration to your GIS and asset management platforms, including import of your pavement labeling/sectioning to our system to guide our surveys and data storage architecture, and export of our data to your choice of GIS and/or pavement management software and file structures (ESRI, etc.). 13 Data Visualization TEE's web -based visualization platform is flexible, user-friendly, and purpose-built for fast, interactive exploration of big geospatial datasets. We offer two visualization programs: a) EXPLORER — which enables users to not only visualize the conditions of the pavement but also the images captured and the corresponding Al predictions of crack type, extent and severity for each road section, and b) ANALYTICS — which can be used to simultaneously visualize and analyze trends across different types of roads, counties, conditions, and distress types. Figure 6 is an example of visualizations produced for Jefferson, MO using ANALYTICS and EXPLORER respectively. As shown in Figure 6, ANALYTICS is able to conveniently summarize data based on the distresses identified and display measures based on the section label (as obtained form the client's existing shape file), and subsequently filtered by pavement type or classification, traffic level, etc. From the EXPLORER dashboard, corresponding camera images and video replays can be accessed in a point -and -click fashion, leading to pop-up boxes showing captured images, filters for superimposing ML-assessed pavement distresses and locations, and a button that when pressed provides access to the 360-degree camera feed. Multiple user accounts for access to the data visualization platform will be provided upon request. Figure 6. Interactive Data Visualization Map Jefferson City Roads Conditions (Heavy Al Platform) Pavement Condition Index (PCI) Rating The Pavement Condition Index, or PCI, is a numerical rating scale for pavements, with 100 representing a perfect pavement condition (new construction) and with scores gradually dropping over time as individual pavement damage areas (distresses) accumulate, degrading the overall serviceability of the pavement. The PCI system requires identifying and characterizing the type, severity, and extent of a range of different distress types for asphalt (flexible) and rigid pavements. `Deduct values' are subtracted from the perfect score of 100 to arrive at the PCI rating for any given section of pavement. In the past, due to the time and expense associated with foot -on -ground surveys, only a randomly sampled, representative subset of the total pavement area was surveyed (involving branches, sections, etc.). However, with our new data collection and analysis architecture, we recommend a full (100% coverage) PCI survey and video capture survey. The continuous rating can then be segmented into sections consistent with the previous survey results, i.e., using your existing shape files or in a custom segmenting arrangement to be mutually established during the project planning phase. PCI ratings will be computed following ASTM D6433-20 procedures for standard road inspection. 14 StreetSaver Update & Database Delivery The Pavement Condition Index (PCI) will be determined and categorized according to the pavement sections specified by the City of Diamond Bar. The final data, including point coordinates, date, direction, surface type, street information, from -to details, type/extent of distress, lane miles, and sidewalk information, will be delivered in a preferred format such as CSV, Excel, Shapefile (ESRI), JSON, etc. We estimate the size of the primary database to be no more than 1 TB, with raw and computed data files delivered for archival purposes on external hard drives. In addition, the PCI data will be updated in the StreetSaver portal to ensure seamless integration with the City of Diamond Bar's existing pavement management system. The output will not only provide detailed GIS- compatible data but also facilitate updates to the city's pavement management plans, enabling more informed decisions about maintenance and repair strategies. Delivered data will include a highly user-friendly and flexible GIS database, tailored specifically to the needs of City of Diamond Bar. Processed images, annotated with distress observations, will be organized into folders labeled with the respective section names for the client's convenience in reviewing the conditions of City of Diamond Bar's roadway. F ZNDAVE.OlO —� �� cs x A=s�tMa��.m.M Executive Dashboard o— IA EMENTAREA CENTERLINEMILES LANE MILES Sf CTIONS •PEER COMPARISON 0 37 51.92 118.45 349 61 25% HISTORICAL PAVEMENT CONDITION TRENDS CURRENT PCI BY: FUNCTION-ClA55 —CURRENT PCI PC, 59 M Collector -PCI. 51 PCI 54 —O &27M24 IOD— Re de bXL..L - PCI: 53 Ro so too "Na 2 since 12l312023 Figure 5. Example of Data Filtering According to a User -defined PCI Range AtlasView AtlasView is an intuitive, cloud -based physical asset management application designed with advanced methodologies to optimize performance and ensure top-teir security. Hosted on AWS (Amazon Web Services), it provides the highest level of data security and reliabiliy. AtlasView offers easy accessibility through common web browsers such as Chrome, Firefox, and Edge, making it available on all web -enabled devices, including Laptops, PCs, tablets, and phones. Should the City decide against the implementation of AtlasView, TEE is fullv capable of completing the services requested usinq StreetSaver. ■oo .: Sc_ uu, ■so.i— - .;I :A ;r, � r"rE� :n ■v-1. POO..2E 1ci ■s'c: fu 2 riyure o. cxarnpie of muvancea color-coaea conamon maps proviaea Dy Atiasview. Condition by Welghted Area Q ■ c ■ Sms'acray ■ Fo s ' ■ r ■sew ■ Fa ■ r� Ir'Sa+,lW M aI Hi —Y I A -A 03I6 R�va�nsat I R�ra I AMy 0 M card i r ■ Far ■-4— w �—ya— # S-- ■ Faked ■ Nmvq— Total jteatment Casl yfi B95,672.93 0 s?tm wow 'se.00eoao.w ss.ow.unw � woo¢w Figure 7. Examples of high-level snapshots of inventory status and condition summary; including key performance trends and maintenance cost needs Cantl[IOtEd I[ispeCSt)fi5 QRsspt fa ��ctiorai ciassr2r�z-n sa r nsP�.�Eo c� ae£.' a-a�2flA3 � I Upload Inspectians Download Template 0 Crag your file here (the format far your csv should match the attached template). O I a-omae j n- 0b' I /� A-goa�nn t6 A-QU2310 8n 0 j A ,.. QM2E ® — 41 O I nvcraa Ac el Figure 8. Import and export inspection & maintencance records easily with Atlas - View 16 ■ _ ■ s.� ■ — R a- u eoa,em.ee xaoa,aao.w n.aoo,mo.00 lZ.00tl,000W mm ,e ■ � a ��ry a Fs a � � �vP^^ a �aK a Fa�� a m ��� ao 2n: 3G 20 Figure 9. Use AtlasView to define flexible policies that establish conditions for triggering maintenance and build detailed multi -year budgets Edit User AV Uj� Yolanda SdTll[fl r_mi'� w:�"�=f�Cw_CO A�ninisE�alor MI Cancel ❑ Fh,l Warne• Lase Warne Email S.— Roles Q ❑ Yolanda Smdh ysmilMp1atlasvl©w.ca U—MIM d Admu tralor ❑ Wlyd... 6irr— wlfda.a ACSIVC R-O9 r Vlcmr 6avld vdavldrdaflasvisw.co Un—firmed Manager Figure 10. Easily manage unlimited users with no additional cost; set varying permission lev- els to ensure transparency while maintaining data integrity 17 WG-d (66 700) Satisfaetory (71 - 65) Fa it (56 - 70) `Poor (41 55) EYery Poor (26 40) WSeri— (11 - 25) OFailetl (0 - 10) §No inspection 1 A„P N I Cabman R",j Pd LE C rd.- h 'ia Ave 51 4 1 Ba Are 52 4 0 t I 53rdAre , y y 11 `�1 I 2-4 11 Figure 11. Example of a public dashboard provided by AtlasView; easily embedded on any website/portal, or share via a direct link ADDITONAL SERVICES TEE offers a wide variety of additional services. Some of those services include: IRI Assessment with TEE's Mobile IRI System ?' Sidewalk evaluation via car > Curb and Gutter evaluations 9 Asset Inventory ��I PU 6050 orienlahon Devito In Enclosure for Data Co11*Ctlon AT Figure 12: Tiger Eye Mobile Data Collection Hardware System 18 � 1 37. . Figure 13: Recified sidewalk images with machine learning based assessment Curb —score =100-(0.5A+B+0.6C+0.75D+E+0.3F) Where: A: Curb&Asphalt _ B: No Curb C: Shattered Curb&Gutter D: Curb&Asphlat_low E: Broken Curb s F: Gutter Figure 14: Curb -and -gutter identification and assessment example (Jefferson City, MO) 0 Sidewalk 1 CurhRasphalt 2 Curb8gutter 3 No curb. 4 drive way 5 ShaQeredl sp curb garter 6 Fire -hydrant 7 Storm drainaaa 8 ADA ramp 9 Manhole 10 Curb asph low 11 I£rokgn curb er 12 Curb beck 13 ADA ramp_no_detector Figure 15: Sampling of asset inventory capabilities TEE is happy to discuss these additional services with the City upon request. 19 APPENDIX Introduction This appendix provides examples of three recent pavement management plan (PMP) updates con- duced by Tiger Eye Engineering. Brief excepts from these project final deliverables are provided (reduced to 2 to 10 pages each), in the interest of brevity. PMP Example #1: Jefferson City, Missouri — TEE data collection, ASTM PCI analysis, Data Dash- board, Integration and Scenario Analysis with Decision Optimization Technology (DOT -US). Note: this analysis led to a $1.6M increase in pavement maintenance investment by the City Council of Jeffer- son. PMP Example #2: Metropolitan Transportation Commission, Pinole, CA - TEE data collection, ASTM PCI analysis, Data Dashboard, Decision Optimization Technology. PMP Example #3: Galesburg, IL - TEE data collection, ASTM PCI analysis, Curb & Gutter Evaluation and Rating, Sidewalk Evaluation and Rating, Asset Extraction and Mapping, Data Dashboard and rnnP EXAMPLE #1: JtFFtKsuN CITY, MISSOURI Pavement Condition & Curb and Gutter Evaluation for Jefferson City, MO TIC3EA EYE BNr3rNec r Tiger Eye Engineering, LLC 1601 S. Providence Rd., 119D Columbia, Missouri, USA, 65211 +001 (217) 369-8370 To: Britt Smith, Operations Division Director Date: May 19, 2023 From: William Buttlar Project: Jefferson City, MO Subject: 2022-2023 Pavement Condition Evaluation Project No: N/A Introduction Tiger Eye Engineering (TEE) is pleased to provide this final project report to the City of Jefferson, MO, for the 2022-2023 Pavement Evaluation and PMP update project. TEE's approach involved a stream- lined data collection process combined with powerful and consistent pavement evaluation through advanced machine learning, data analytics, and data visualization. We also calibrated and validated TEE results to the local conditions in Mid -MO. This approach provided Jefferson City the information required to make data -driven, accurate assessments of its road infrastructure network and a system to evaluate and fine-tune maintenance decisions. This is consistent with the City's desire to elevate the pavement condition across its network via optimized allocation of its maintenance funding over a 10-year period. There are two appendices to this report: Appendix A provides details on TEE's IRI measurement system, while Appendix B provides a brief summary of the analysis conducted to pro- vide a 10-year pavement management plan (PMP) to the city, featuring a sophisticated, non -linear optimization analysis completed using the US -DOT (decision optimization technology) software suit Data Collection TEE pavement imaging system includes a high -resolution 360-degree camera, a stereo camera, a GPS unit, and accelerometers to capture pavement roughness. The video frames is labeled with GPS information, which are then used by TEE software to extract images at specified intervals to remove duplication and to cover the pavement network continuously (Figure 1 and 2). The Insta 360 cameras are used to collect a 360-degree street view video dashboard. Appendix A provides details on TEE IRI capture and evaluation system. a) b) c) Figure 1. a) 4k Camera, b) Dual Lenses Camera, and c) GPS Unit Frame 1 Frame 98 Frame 151 • • • Frame {n) Distance:5m Distance:5m Distance:5m Figure 1. Converting Video Frames to .jpg Images at Specified Intervals (5 m) using GPS Data Figure 2. TEE Data Collection System Automated Evaluation of Captured Pavement Images Pavement condition assessment provides critically needed information allowing owner -agencies to make more cost-effective and consistent decisions as they manage their network of urban and/ or rural pavements. Generally, pavement distress inspections are performed using sophisticated data collection vehicles and/or foot -on -ground surveys. In either approach, the process of distress detection is sub -optimal, as it inherently contains human bias, is very costly and inefficient, and can introduce on -site inspector safety risks. The Tiger Eye Engineering (TEE) automated pavement evaluation software suite was developed by coding and integrating several machine learning and deep learning techniques for distress detection and pavement condition assessment (Figure 3). STEP 2 Training and prediction ,CO# STEP Data collection STEP 3 Type Extent Severity STEP 4 STEP 6 PCI Strategic management plan V 0%. STEP 5 Map visualization Artificial Intelligence & Deep Learning Figure 3. Application of Al to Detect Type, Extent and Severity of Distresses In Figure 4, samples of detections from 28 distinct distress types on both flexible and rigid pavement are displayed. The Al models have annotated the images, indicating the type and extent of each distress. ¢l , Figure 4. Representative Images and Automated Distress Detection Results for Flexible and Rigid Pavement Once the Al algorithms detect the type, extent, and severity of the distresses, the Pavement Condition Index (PCI) is then calculated using standardized equations. The PCI is represented on a scale ranging from 0 to 100, where higher values signify a superior condition of the pavement (Figure 5). 100 PCI Condition I #de#0 io df so .0 100 80 I i / ISO i OPr. r�f......••• a� If �r ��/ 85factory m Off // d ; '' Log. (q1) �� � I 60.............. .............�/... ..'/ � �' �� — — — Linear (q1) / / E''/ ---Poly. (q2) 70 Fair / / ---Poly. (q3) 40 // If / /ir / If, —Poly. (q4) 55 Poor If // % /� Poly. (q5) 40 20 If If/ If — — — Poly. (q6) Very Poor // a —Poly. (q7) 25 0 j Serious 0 20 40 60 80 100 120 140 160 180 200 10 Total deducted values Failed 0 Figure 5. Deducted Values Curves and PCI Scale Curb and Gutter Evaluation Curb and gutter condition evaluation involves assessing the state of curbs and gutters along city streets. In certain areas, curbs may be absent, or the pavement layer may have been repeatedly overlaid, resulting in a lack of elevation between the surface and the curb. To identify such areas, a curb evaluation is conducted to flag these anomalies. This evaluation helps identify locations where curbs are missing or where pavement overlays have caused issues with the curb, providing valuable information for maintenance and improvement efforts. Here are the categories of the detections: • Curb & gutter • No curb • Curb & asphalt • Drive way The curb score is calculated based on the following formula: S= (T-N-0.5*A)/T*100) S = Curb score T= total length N=no curb length A= curb & asphalt length 0 Ca a ti O N<<<►>»N Figure 6. Annotation of Curb and Gutter Categories Visualization Dashboards Pavement condition of the evaluated sections (2022 survey and previous year data results) is visualized on TEE's interactive visualization platforms. Examples of TEE two main visualization programs are shown in Figures 7 and 8, where `heat maps' of pavement condition or other user -selectable data items can be holistically viewed. The statistics of the distresses can be categorized and shown based on the city area (sector, council, segment), filtered by pavement type or classification, traffic level, etc. Also, corresponding camera images and video replays can be accessed in a point -and -click fashion, leading to pop-up boxes showing captured images, filters for superimposing ML-assessed pavement distresses and locations, and videos of driven pavement segments as catalogued by the Jefferson City. Multiple user accounts for access to the data visualization platform will be provided upon request. Figure 7. Interactive Data Visualization Map Jefferson City Roads Conditions (Heavy Al Platform) Within the TEE dashboard, every data point is linked to an accompanying top - down image showcasing the detected distress types, extent, and PCI for both the image and the corresponding section. Additionally, an embedded link to the 360- view dashboard is provided, offering a comprehensive visual perspective. Figure 8. Image Integrated Interactive Data Visualization Map Jefferson City Roads City (TEE Platform) Appendix A IRI Assessment with TEE's Mobile App TigerEye's mobile application (interface shown in Figure Al) is used to estimate the roughness index (IRI). TheAPP first captures vehicles'speed, acceleration, rotation, heading, location (latitude, longitude) from the phone's on -board sensors. The APP has been calibrated to IRI: data was collected at different speeds, on different types of roads and a machine learning algorithm has been developed to correlate the data captured from the phone to actual IRI data. The TigerEye APP will output live roughness information when WiFi is available. In WiFi-dead zones, the APP stores vehicle vibration and mobility information in the smartphone storage system. Capture Video Aura $av6 4 DD.Da00 •'� Sow 0 mph . Figure Al. Mobile App Interface for Capturing IRI Figure A2 shows the accuracy of the mobile apps' estimated IRI on different road sections and vehicle speeds. The average root mean squared error ranges between 1.6 and 5.8 inches per mile. The accuracy is lowest for low -speed roads, with average RMSE ranging between 3 and 8 inches per mile. Consistently high accuracies are observed when speeds are greater than 35 mph. 120 100 N a� U OD 9 60. 40 E a 70 60 a� s U C 50 U 40 30 20 30 40 Distance (miles) • • • • . -squared=0.79 • • • �• 04,0 +0 • 30 40 50 60 70 Predicted iri (inchcs/mile) Figure A2. Accuracy of Tiger Eye APP for Estimating IRI The software -based approached is calibrated and validated against roughness captured by a Class 1 Inertial Profiler that satisfies the ASTM-950 — 98 Standard, "Standard Test Method for Measuring the Longitudinal Profile of Traveled Surfaces with an Accelerometer Established Inertial Profiling. Appendix B DOT (Decision Optimization Technology)TM is next -generation asset investment planning (AIP) software designed to help cites and municipalities maximize the impact of the tax dollars and plot a course for the sustainable growth of the whole network. A brief summary of the analysis conducted and delivered to the City of Jefferson is now provided. Using DOTTMIStrue optimization analysis, the City would: • Decrease in overall network PCI dropped from 71 to 62 by the end of the 10 yr/$1.5M budget plan • Larger decrease in overall network PCI dropped from 71 to 59 by the end of the 10 yr/$1 M budget plan • Do nothing scenario results in an overall network PCI of 48 by the end of the 10 yr/$0 budget plan • To maintain current conditions of Locals: 70, Collectors: 72, Arterials: 72, Freeway: 74, an investment of approximately $2.5M annually is needed over the next 10 years • To hit target conditions of Locals: 65, Collectors: 75, Arterials: 80, Freeway: 80, an investment of approximately $2.75M annually is needed over the next 10 year Figure B1. Jeff City CIP $1 M 5 Years DOT........ ............ ........... Reeds - Results AEI(T rvo( Mfin.lN.YfAw(n NEiw.lN.Isvl[w S.SSET INYFMTMY ) :. DOT p1(.I..N Y...wrf..ryw11C..91961. -- p O ® O sz Roads -Results ��Or01� wetwarN c.naiu.n ® l U N[Tw.w.v[!vl[w '�'a^^^( ---•..mn rt1.w.f sw. wry rtT..(.scae..., .ov+ N[TrrorNwsvrEw — r — e .SS[i,NY[HT..T Ill .n..vsw v iCl-l..•I NH1MMk C.nONbn a[ro.n !°•mow'( +-wool[ rt..C(.LSw1. 1.Yn rtT.r(w Esaebn. wws wort 4 � PMP EXAMPLE #2: PINOLE, CALIFORNIA (W/MTC) (Example of TEE pavement condition data integration with StreetSaver pavement management software As part of this project, PCI data has been successfully integrated into the StreetSaver software, de- veloped by the Metropolitan Transportation Commission (MTC). StreetSaver, a widely adopted pave- ment management tool, allows municipalities to track, analyze, and optimize pavement maintenance strategies. Updating the PCI data within StreetSaver ensured seamless alignment with the City of Pinole's existing pavement management system, resulting in an actionable and accessible PMP. This integration has enhanced decision -making for maintenance and repair strategies by leveraging StreetSaver's advanced analysis and reporting capabilities. The deliverables included a user-friendly, GIS-compatible database tailored to the specific needs of the City of Pinole. Processed images, annotated with observed pavement distresses, were systemat- ically organized into folders named after their respective sections, enabling convenient access for city staff to review roadway conditions. Additionally, the data now supports the City's pavement manage- ment plans by facilitating informed prioritization and budgeting for rehabilitation efforts (- 2NOAVE-DID i d g Asset MAnaaemerlt Ex"v0" Dashboard a PAVEMENT AREA CENTERLINE MILES LANE MILES SECTIONS •PEERCOMPARISON 0.37 51.92 118.45 349 61 25% HISTORICAL PAVEMENT CONDITION TRENDS CURRENT PCI DY: FUNCTIONAL CLASS "CURRENT PCI Art riaL - PCI: 59 40 (O • • _ _• _ _.• Calle -PCI: 51 1 zo PCI 54 -0 8/21/2024 100-'- Ra &,WL(L—L-PCI: 53 '�j 2 since 12/31/2023 Figure 1. Example of Data Filtering According to a User -defined PCI Range, and Street Saver Dashboard PMP EXAMPLE #3: GALESBURG, IL (Example of multi -asset, infrastructure evaluation) In this section, we provide a graphical overview of a multi -asset, infrastructure evaluation project conducted for the City of Galesburg, IL. This project involved TEE data collection with our pavement inspection vehicle and ebike fleet (for sidewalk evaluation), ASTM PCI analysis, curb & gutter evaluation and rating, sidewalk evaluation and rating, asset extraction and mapping, data dashboarding and streamlined `big data' analytics. A series of screenshots from our multi -tab, layered visualization dashboard suite is now provided. Example 1: Big data dashboard showing overall pavement network ratings and section -by - section information, including pavement condition index (PCI), and filtering by type, extent, and severity of distresses. Example 2: Road network dashboard, showing access to individual video snapshots and annotated distress identification results Example 3: Curb -and -gutter type identification and rating dashboard Example 4: Sidewalk condition evaluation and rating dashboard CERTIFICATE OF LIABILITY INSURANCE DATE /Y) /16/20242024 12/16 THIS CERTIFICATE IS ISSUED AS A MATTER OF INFORMATION ONLY AND CONFERS NO RIGHTS UPON THE CERTIFICATE HOLDER. THIS CERTIFICATE DOES NOT AFFIRMATIVELY OR NEGATIVELY AMEND, EXTEND OR ALTER THE COVERAGE AFFORDED BY THE POLICIES BELOW. THIS CERTIFICATE OF INSURANCE DOES NOT CONSTITUTE A CONTRACT BETWEEN THE ISSUING INSURER(S), AUTHORIZED REPRESENTATIVE OR PRODUCER, AND THE CERTIFICATE HOLDER. IMPORTANT: If the certificate holder is an ADDITIONAL INSURED, the policy(ies) must be endorsed. If SUBROGATION IS WAIVED, subject to the terms and conditions of the policy, certain policies may require an endorsement. 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