{"id":2464,"date":"2026-06-01T03:07:28","date_gmt":"2026-06-01T10:07:28","guid":{"rendered":"https:\/\/labs.engineering.asu.edu\/pavement\/?page_id=2464"},"modified":"2026-06-12T16:58:48","modified_gmt":"2026-06-12T23:58:48","slug":"construction-quality-monitoring-of-hot-mix-asphalt-using-uav-thermal-imagery","status":"publish","type":"page","link":"https:\/\/labs.engineering.asu.edu\/pavement\/projects\/construction-quality-monitoring-of-hot-mix-asphalt-using-uav-thermal-imagery\/","title":{"rendered":"Construction Quality Monitoring of Hot Mix Asphalt Using UAV Thermal Imagery\u00a0"},"content":{"rendered":"<div class=\"uds-hero-md alignfull has-btn-row \" style=\"margin-bottom:var(--wp--preset--spacing--uds-size-8);\"><div class=\"hero-overlay\"><\/div><img loading=\"lazy\" decoding=\"async\" width=\"1306\" height=\"1700\" src=\"https:\/\/labs.engineering.asu.edu\/pavement\/wp-content\/uploads\/sites\/189\/2024\/10\/UAV_NCIT-e1780308264943.jpeg\" class=\"hero\" alt=\"\" \/><div class=\"acf-innerblocks-container\">\n\n\n\n<h1 class=\"wp-block-heading has-white-color has-text-color\"><span class=\"highlight-gold\"><strong><strong><strong>Construction Quality Monitoring of Hot Mix Asphalt Using UAV Thermal Imagery&nbsp;<\/strong><\/strong><\/strong><\/span><\/h1>\n\n\n\n<div class=\"wp-block-group content is-layout-flow wp-block-group-is-layout-flow\">\n<p class=\"is-style-lead has-white-color has-text-color wp-block-paragraph\"><mark style=\"background-color:#191919\" class=\"has-inline-color has-white-color\">\ud83d\udce8 Principal Investigator :<strong>&nbsp;<\/strong><a href=\"https:\/\/search.asu.edu\/profile\/3503404\"><\/a><a href=\"https:\/\/search.asu.edu\/profile\/3503404\">Hasan Ozer<\/a><br>\ud83e\udd1d Sponsor:&nbsp;<strong>&nbsp;<\/strong>National Center for Infrastructure Transformation (NCIT)<br>\ud83d\udcc5 Timeline:&nbsp;2022 &#8211; Ongoing<\/mark><\/p>\n<\/div>\n\n<\/div><\/div>\n\n\n<div class=\"wp-block-group is-layout-flow wp-block-group-is-layout-flow\">\n<h3 class=\"wp-block-heading\" style=\"margin-top:4rem;margin-bottom:2rem\"><span class=\"highlight-black\">Highlights<\/span><\/h3>\n\n\n\n<style>\n.hl-wrap{margin:20px 0;}\n.hl-bar{height:2px;background:#eee;margin-bottom:0;}\n.hl-fill{height:100%;width:0%;background:rgb(255,198,39);transition:width 0.1s linear;}\n.hl-box{border:1px solid #ddd;border-top:none;padding:24px 28px 20px;position:relative;}\n.hl-slide{position:absolute;inset:0;padding:24px 28px 20px;opacity:0;transition:opacity 0.8s ease;pointer-events:none;}\n.hl-slide.active{opacity:1;pointer-events:auto;position:relative;}\n.hl-track{position:relative;min-height:120px;}\n.hl-num{font-size:12px;letter-spacing:2px;color:#999;text-transform:uppercase;margin-bottom:10px;}\n.hl-title{font-size:20px;font-weight:700;color:#1a1a1a;margin-bottom:10px;}\n.hl-text{font-size:16px;color:#444;line-height:1.7;}\n.hl-dots{display:flex;gap:6px;margin-top:16px;}\n.hl-dot{width:8px;height:8px;border-radius:50%;background:#ccc;border:none;cursor:pointer;padding:0;transition:all 0.3s;}\n.hl-dot.active{background:#333;width:20px;border-radius:4px;}\n.hl-svg{position:absolute;inset:0;width:100%;height:100%;pointer-events:none;overflow:visible;}\n.hl-top{fill:none;stroke:rgb(255,198,39);stroke-width:5;stroke-linecap:round;}\n.hl-bot{fill:none;stroke:rgb(255,198,39);stroke-width:5;stroke-linecap:round;}\n<\/style>\n\n<div class=\"hl-wrap\">\n  <div class=\"hl-box\" id=\"hlbox\">\n    <svg class=\"hl-svg\" id=\"hlsvg\">\n      <path class=\"hl-top\" id=\"hltop\"\/>\n      <path class=\"hl-bot\" id=\"hlbot\"\/>\n    <\/svg>\n\n    <div class=\"hl-track\">\n\n      <div class=\"hl-slide active\">\n  <div class=\"hl-num\">01 \/ 03 \u2014 Objective<\/div>\n  <div class=\"hl-title\">Automated UAV-Based Construction Quality Monitoring for Asphalt Pavement<\/div>\n  <div class=\"hl-text\">Developed and validated an automated UAV-based construction quality monitoring protocol for freshly placed asphalt pavement, using infrared thermal imagery and deep learning object detection to assess thermal segregation and compaction uniformity in real time across entire paving operations.<\/div>\n<\/div>\n\n<div class=\"hl-slide\">\n  <div class=\"hl-num\">02 \/ 03 \u2014 Key Finding<\/div>\n  <div class=\"hl-title\">UAV Thermal Cameras and YOLOv8 Deliver Real-Time Compaction Insight Across 13 Sites<\/div>\n  <div class=\"hl-text\">UAV-mounted thermal cameras provide significantly broader spatial coverage and actionable real-time insight into non-uniform cooling patterns and insufficient roller passes than existing paver-mounted or ground-based methods. A YOLOv8-based object detection model successfully tracked roller movements across 13 Arizona construction sites, automatically computing pass counts, speed, and coverage.<\/div>\n<\/div>\n\n<div class=\"hl-slide\">\n  <div class=\"hl-num\">03 \/ 03 \u2014 Impact<\/div>\n  <div class=\"hl-title\">Non-Intrusive QA Tool with Filed Intellectual Property Patent<\/div>\n  <div class=\"hl-text\">The developed protocol provides contractors and agencies with a non-intrusive quality assurance tool that can identify construction inconsistencies that hinder long-term pavement durability, and resulted in a filed intellectual property patent for the monitoring framework.<\/div>\n<\/div>\n\n    <\/div>\n    <div class=\"hl-dots\">\n      <button class=\"hl-dot active\" onclick=\"hlGo(0)\"><\/button>\n      <button class=\"hl-dot\" onclick=\"hlGo(1)\"><\/button>\n      <button class=\"hl-dot\" onclick=\"hlGo(2)\"><\/button>\n    <\/div>\n  <\/div>\n<\/div>\n\n<script>\n(function(){\n  var slides=document.querySelectorAll('.hl-slide'),dots=document.querySelectorAll('.hl-dot'),box=document.getElementById('hlbox'),top=document.getElementById('hltop'),bot=document.getElementById('hlbot'),cur=0,total=slides.length,dur=7000,start=null;\n  function setSize(){\n    var w=box.offsetWidth,h=box.offsetHeight,mx=0,my=h\/2,ex=w,ey=h\/2;\n    top.setAttribute('d',\"M \"+mx+\",\"+my+\" L 0,0 L \"+w+\",0 L \"+ex+\",\"+ey);\n    bot.setAttribute('d',\"M \"+mx+\",\"+my+\" L 0,\"+h+\" L \"+w+\",\"+h+\" L \"+ex+\",\"+ey);\n    var tl=top.getTotalLength(),bl=bot.getTotalLength();\n    top.style.strokeDasharray=tl;top.style.strokeDashoffset=tl;\n    bot.style.strokeDasharray=bl;bot.style.strokeDashoffset=bl;\n  }\n  function update(p){\n    top.style.strokeDashoffset=top.getTotalLength()*(1-p);\n    bot.style.strokeDashoffset=bot.getTotalLength()*(1-p);\n  }\n  function show(i){\n    slides[cur].classList.remove('active');dots[cur].classList.remove('active');\n    cur=(i+total)%total;\n    slides[cur].classList.add('active');dots[cur].classList.add('active');\n    start=null;setSize();\n  }\n  function tick(ts){\n    if(!start)start=ts;\n    var p=Math.min((ts-start)\/dur,1);\n    update(p);if(p>=1)show(cur+1);\n    requestAnimationFrame(tick);\n  }\n  window.hlGo=function(i){show(i);};\n  setSize();requestAnimationFrame(tick);\n  window.addEventListener('resize',setSize);\n})();\n<\/script>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"highlight-black\">Introduction<\/span><\/h3>\n\n\n\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-930feb06 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<p class=\"wp-block-paragraph\">Getting the compaction right during asphalt paving is one of the most consequential decisions in pavement construction. Proper compaction improves in-place density, and empirical evidence suggests that even a one percent increase in density can translate to at least a 10 percent longer pavement service life.&nbsp;Yet&nbsp;monitoring&nbsp;the compaction process is challenging because&nbsp;the&nbsp;paving crew&nbsp;operates&nbsp;over hundreds of feet of roadway, coordinating roller passes, speed, and sequencing as the&nbsp;paved&nbsp;mat cools quickly.&nbsp;Areas that cool too quickly may not achieve target density before the mat stiffens, leaving behind hidden zones of&nbsp;reduced&nbsp;performance that will&nbsp;develop&nbsp;as early distress years down the road.&nbsp;<\/p>\n<\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"741\" height=\"240\" src=\"https:\/\/labs.engineering.asu.edu\/pavement\/wp-content\/uploads\/sites\/189\/2026\/05\/image-17.png\" alt=\"\" class=\"wp-image-2466\" srcset=\"https:\/\/labs.engineering.asu.edu\/pavement\/wp-content\/uploads\/sites\/189\/2026\/05\/image-17.png 741w, https:\/\/labs.engineering.asu.edu\/pavement\/wp-content\/uploads\/sites\/189\/2026\/05\/image-17-500x162.png 500w\" sizes=\"auto, (max-width: 741px) 100vw, 741px\" \/><\/figure>\n<\/div>\n<\/div>\n\n\n\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-930feb06 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<p class=\"wp-block-paragraph\">Existing&nbsp;intelligent construction&nbsp;technologies&nbsp;(ICTs)&nbsp;for&nbsp;monitoring&nbsp;thermal&nbsp;and compaction&nbsp;uniformity during paving have meaningful limitations.&nbsp;Paver-mounted thermal profilers&nbsp;(PMTP) capture temperature data only in the narrow strip directly behind the paver screed, missing what happens to the mat after placement. Intelligent Compaction (IC) systems track roller position and&nbsp;stiffness,&nbsp;but&nbsp;exhibit&nbsp;inconsistent correlations&nbsp;and require expensive instrumented rollers. This project, funded by the National Center for Infrastructure Transformation (NCIT) and led by Dr. Hasan Ozer with co-authors Naaga Vedula and Masih Beheshti at Arizona State University, developed an alternative: a UAV equipped with an infrared thermal camera, flying above the paving operation and transmitting real-time thermal maps of the entire freshly placed mat across 13 Arizona construction sites.&nbsp;<\/p>\n<\/div>\n<\/div>\n\n\n\n<h3 class=\"wp-block-heading\" style=\"margin-top:2rem;margin-bottom:2rem\"><strong><strong><strong><strong><span class=\"highlight-black\">Methodology and Framework<\/span><\/strong><\/strong><\/strong><\/strong><\/h3>\n\n\n\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-930feb06 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-930feb06 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"624\" height=\"570\" src=\"https:\/\/labs.engineering.asu.edu\/pavement\/wp-content\/uploads\/sites\/189\/2026\/05\/image-19.png\" alt=\"\" class=\"wp-image-2468\" srcset=\"https:\/\/labs.engineering.asu.edu\/pavement\/wp-content\/uploads\/sites\/189\/2026\/05\/image-19.png 624w, https:\/\/labs.engineering.asu.edu\/pavement\/wp-content\/uploads\/sites\/189\/2026\/05\/image-19-500x457.png 500w\" sizes=\"auto, (max-width: 624px) 100vw, 624px\" \/><\/figure>\n<\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<p class=\"wp-block-paragraph\">The study was structured in two phases. Phase 1 (spring-summer 2023) established the data collection protocol using a DJI Matrice 300 RTK UAV equipped with an H20N thermal camera&nbsp;&#8211;&nbsp;a dual-infrared&nbsp;sensor system with 2\u00d7 and 8\u00d7 optical zoom and up to 32\u00d7 digital zoom, capable of capturing 640\u00d7512-pixel radiometric thermal images. Eight paving job sites across the Phoenix metropolitan area were visited during active paving operations. The UAV was flown at 30 m (~100 ft) altitude, covering a 200-foot window behind the paver. A protocol was developed to divide each image into 7 horizontal strips and 13 data points per 50-foot sublot, enabling systematic temperature analysis of the mat surface.&nbsp;A&nbsp;new metric, the Mat Differential Matrix (MDM), was developed to locally quantify thermal differentials.&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Phase 2 (spring-summer&nbsp;2024) automated the data collection and analysis pipeline using the DJI Thermal Software Development Kit (SDK) to extract radiometric temperature data at 2-second intervals without operator intervention.&nbsp;Roller-tracking&nbsp;capability,&nbsp;where&nbsp;a YOLOv8 deep learning object detection model was trained to&nbsp;identify&nbsp;both the freshly paved mat and compaction rollers in thermal images,&nbsp;was added in this phase. The trained model enabled continuous, automated tracking of roller position, speed, pass counts, and coverage patterns,&nbsp;which&nbsp;previously&nbsp;required&nbsp;expensive GPS-equipped instrumented roller systems.&nbsp;<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n<h3 class=\"wp-block-heading\"><strong><strong><strong><strong><span class=\"highlight-black\">Key Findings<\/span><\/strong><\/strong><\/strong><\/strong><\/h3>\n<\/div>\n\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n\n\n\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-930feb06 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<p class=\"wp-block-paragraph\"><strong>Thermal Segregation Patterns Across Arizona Sites<\/strong>&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Phase 1 analysis revealed that thermal non-uniformity was present&nbsp;to some degree at&nbsp;all eight sites, though severity varied considerably. Several&nbsp;non-uniform&nbsp;thermal&nbsp;patterns were&nbsp;identified. Longitudinal cold streaks appeared at sites&nbsp;with&nbsp;uneven temperature distribution&nbsp;caused&nbsp;by the&nbsp;gap between the paver auger&nbsp;gearbox and the paved mat. V-shaped temperature anomalies (cooler zones radiating diagonally behind the paver) were&nbsp;observed&nbsp;at one site and traced to a specific&nbsp;issue with the&nbsp;closed hopper wings, which were holding cold material,&nbsp;demonstrating&nbsp;the diagnostic potential of the UAV thermal-mapping&nbsp;approach.&nbsp;Cool-down analysis using the UAV imagery quantified how rapidly&nbsp;different parts&nbsp;of the mat lost temperature, providing insight into where compaction windows were narrowing due to early cooling.&nbsp;<\/p>\n<\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"483\" height=\"357\" src=\"https:\/\/labs.engineering.asu.edu\/pavement\/wp-content\/uploads\/sites\/189\/2026\/05\/image-16.png\" alt=\"\" class=\"wp-image-2465\"\/><\/figure>\n<\/div>\n<\/div>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Automated Roller Tracking and Compaction Analysis<\/strong><\/p>\n\n\n\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-930feb06 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<p class=\"wp-block-paragraph\">The Phase 2 YOLOv8 object detection model achieved high accuracy in\u00a0identifying\u00a0and tracking rollers in thermal aerial images, even under challenging conditions like partial occlusion and varying roller orientations. The model was trained using a combination of real images from Phase 1 and\u00a0synthetic data augmented with real images, and the best-performing model (YOLOv8s-obb) achieved an intersection-over-union (IoU)\u00a0score sufficient for reliable roller bounding-box detection across diverse site conditions. From the roller detections, the compaction analysis algorithm computed roller pass counts for each section of the mat, roller speed, and\u00a0identified\u00a0sections with insufficient passes\u00a0relative\u00a0to the job mix formula rolling pattern requirements. A case study at an experimental Phoenix-area site\u00a0demonstrated\u00a0the framework&#8217;s full\u00a0capabilities, with two adjacent lanes\u00a0paved and\u00a0monitored\u00a0simultaneously. The\u00a0UAV data revealed\u00a0that the west lane had more non-uniform cooling and areas with insufficient roller passes, which were confirmed by core density data collected 1 and 6\u00a0months later.\u00a0<\/p>\n<\/div>\n\n\n\n<div class=\"wp-block-column is-vertically-aligned-center is-layout-flow wp-block-column-is-layout-flow\">\n<figure class=\"wp-block-video aligncenter\"><video height=\"322\" style=\"aspect-ratio: 816 \/ 322;\" width=\"816\" autoplay loop muted src=\"https:\/\/labs.engineering.asu.edu\/pavement\/wp-content\/uploads\/sites\/189\/2026\/06\/Roller_Tracking.mp4\" playsinline><\/video><\/figure>\n<\/div>\n<\/div>\n\n\n\n<p class=\"wp-block-paragraph\">\u00a0<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Patent and Industry Implications<\/strong>&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The integrated thermal-compaction monitoring protocol developed in this study was&nbsp;submitted&nbsp;for intellectual property protection through an ASU patent filing, reflecting its potential for commercial deployment. The protocol offers contractors a non-intrusive,&nbsp;unified&nbsp;monitoring system that does not require modifications to rollers or the paver. It&nbsp;can provide real-time feedback to the paving crew about emerging problem areas before the mat cools below the compaction temperature threshold.&nbsp;It can also be&nbsp;used as a training tool for the operations crew to&nbsp;help mitigate the recurrence of these errors at&nbsp;subsequent&nbsp;job sites.&nbsp;<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"highlight-black\">Publications<\/span><\/h3>\n\n\n\n<style>\n.pub-list{margin:20px 0;}\n.pub-card{border:1px solid #e5e5e5;border-left:3px solid #e5e5e5;padding:18px 20px;margin-bottom:12px;background:#fff;position:relative;overflow:hidden;opacity:0;transform:translateY(24px);transition:opacity 0.6s ease,transform 0.6s ease;}\n.pub-card.visible{opacity:1;transform:translateY(0);}\n.pub-card::before{content:'';position:absolute;top:0;left:0;width:3px;height:0%;background:rgb(255,198,39);transition:none;}\n.pub-card::after{content:'';position:absolute;bottom:0;left:0;width:0%;height:3px;background:rgb(255,198,39);transition:none;}\n.pub-card.traced::before{height:100%;transition:height 1s ease;}\n.pub-card.traced-bottom::after{width:100%;transition:width 1s ease 1s;}\n.pub-top{display:flex;align-items:flex-start;gap:12px;}\n.pub-year{font-size:11px;font-weight:700;letter-spacing:2px;color:#888;border:1px solid #ddd;padding:3px 8px;border-radius:3px;white-space:nowrap;margin-top:2px;}\n.pub-title{font-size:14px;font-weight:700;color:#1a1a1a;line-height:1.4;margin-bottom:5px;}\n.pub-authors{font-size:12px;color:#666;margin-bottom:4px;}\n.pub-journal{font-size:12px;color:#444;font-style:italic;margin-bottom:10px;}\n.pub-link{font-size:11px;letter-spacing:1px;text-transform:uppercase;color:#333;text-decoration:none;border-bottom:1px solid #ccc;padding-bottom:1px;transition:border-color 0.2s;}\n.pub-link:hover{border-color:#333;}\n<\/style>\n\n<div class=\"pub-list\">\n\n  <div class=\"pub-card\">\n    <div class=\"pub-top\">\n      <div class=\"pub-year\">2024<\/div>\n      <div>\n        <div class=\"pub-title\">Thermal Profiling of Asphalt Pavement Construction Using Unmanned Aerial Vehicle<\/div>\n        <div class=\"pub-authors\">Vedula, N.V., Beheshti, M., Al-Alawi, O., &#038; Ozer, H.<\/div>\n        <div class=\"pub-journal\">Transportation Research Record: Journal of the Transportation Research Board, 2678(11), 170\u2013186<\/div>\n        <a class=\"pub-link\" href=\"https:\/\/doi.org\/10.1177\/03611981241239957\" target=\"_blank\">View Paper \u2192<\/a>\n      <\/div>\n    <\/div>\n  <\/div>\n\n  <div class=\"pub-card\">\n    <div class=\"pub-top\">\n      <div class=\"pub-year\">2025<\/div>\n      <div>\n        <div class=\"pub-title\">Automated framework for evaluating asphalt pavement construction using UAV imagery<\/div>\n        <div class=\"pub-authors\">Vedula, N., Beheshti, M., Madasu, S., &#038; Ozer, H.<\/div>\n        <div class=\"pub-journal\">Automation in Construction, 180, 106498<\/div>\n        <a class=\"pub-link\" href=\"https:\/\/doi.org\/10.1016\/j.autcon.2025.106498\" target=\"_blank\">View Paper \u2192<\/a>\n      <\/div>\n    <\/div>\n  <\/div>\n\n<\/div>\n\n<script>\nvar cards=document.querySelectorAll('.pub-card');\nvar obs=new IntersectionObserver(function(entries){\n  entries.forEach(function(e){\n    if(e.isIntersecting){e.target.classList.add('visible');obs.unobserve(e.target);}\n  });\n},{threshold:0.1});\ncards.forEach(function(c){obs.observe(c);});\nfunction traceCard(card,delay){\n  setTimeout(function(){\n    card.classList.add('traced');\n    setTimeout(function(){card.classList.add('traced-bottom');},1000);\n  },delay);\n}\nvar traceObs=new IntersectionObserver(function(entries){\n  entries.forEach(function(e){\n    if(e.isIntersecting){\n      var idx=Array.from(cards).indexOf(e.target);\n      traceCard(e.target,idx*1500);\n      traceObs.unobserve(e.target);\n    }\n  });\n},{threshold:0.1});\ncards.forEach(function(c){traceObs.observe(c);});\n<\/script>\n\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n","protected":false},"excerpt":{"rendered":"<p class=\"mb-2\">Highlights 01 \/ 03 \u2014 Objective Automated UAV-Based Construction Quality Monitoring for Asphalt Pavement Developed and validated an automated UAV-based construction quality monitoring protocol for freshly placed asphalt pavement, using infrared thermal imagery and deep learning object detection to assess thermal segregation and compaction uniformity in real time across entire paving operations. 02 \/ 03&#8230;<\/p>\n","protected":false},"author":498,"featured_media":0,"parent":1848,"menu_order":3,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"footnotes":""},"class_list":["post-2464","page","type-page","status-publish","hentry"],"acf":[],"_links":{"self":[{"href":"https:\/\/labs.engineering.asu.edu\/pavement\/wp-json\/wp\/v2\/pages\/2464","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/labs.engineering.asu.edu\/pavement\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/labs.engineering.asu.edu\/pavement\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/labs.engineering.asu.edu\/pavement\/wp-json\/wp\/v2\/users\/498"}],"replies":[{"embeddable":true,"href":"https:\/\/labs.engineering.asu.edu\/pavement\/wp-json\/wp\/v2\/comments?post=2464"}],"version-history":[{"count":4,"href":"https:\/\/labs.engineering.asu.edu\/pavement\/wp-json\/wp\/v2\/pages\/2464\/revisions"}],"predecessor-version":[{"id":2563,"href":"https:\/\/labs.engineering.asu.edu\/pavement\/wp-json\/wp\/v2\/pages\/2464\/revisions\/2563"}],"up":[{"embeddable":true,"href":"https:\/\/labs.engineering.asu.edu\/pavement\/wp-json\/wp\/v2\/pages\/1848"}],"wp:attachment":[{"href":"https:\/\/labs.engineering.asu.edu\/pavement\/wp-json\/wp\/v2\/media?parent=2464"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}