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Use of Automated Machine Guidance within the Transportation Industry (2018)

Chapter: Chapter 8: Impact of AMG on Earthwork Quantities

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Suggested Citation:"Chapter 8: Impact of AMG on Earthwork Quantities." National Academies of Sciences, Engineering, and Medicine. 2018. Use of Automated Machine Guidance within the Transportation Industry. Washington, DC: The National Academies Press. doi: 10.17226/25084.
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Suggested Citation:"Chapter 8: Impact of AMG on Earthwork Quantities." National Academies of Sciences, Engineering, and Medicine. 2018. Use of Automated Machine Guidance within the Transportation Industry. Washington, DC: The National Academies Press. doi: 10.17226/25084.
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Suggested Citation:"Chapter 8: Impact of AMG on Earthwork Quantities." National Academies of Sciences, Engineering, and Medicine. 2018. Use of Automated Machine Guidance within the Transportation Industry. Washington, DC: The National Academies Press. doi: 10.17226/25084.
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Suggested Citation:"Chapter 8: Impact of AMG on Earthwork Quantities." National Academies of Sciences, Engineering, and Medicine. 2018. Use of Automated Machine Guidance within the Transportation Industry. Washington, DC: The National Academies Press. doi: 10.17226/25084.
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Suggested Citation:"Chapter 8: Impact of AMG on Earthwork Quantities." National Academies of Sciences, Engineering, and Medicine. 2018. Use of Automated Machine Guidance within the Transportation Industry. Washington, DC: The National Academies Press. doi: 10.17226/25084.
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Suggested Citation:"Chapter 8: Impact of AMG on Earthwork Quantities." National Academies of Sciences, Engineering, and Medicine. 2018. Use of Automated Machine Guidance within the Transportation Industry. Washington, DC: The National Academies Press. doi: 10.17226/25084.
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Suggested Citation:"Chapter 8: Impact of AMG on Earthwork Quantities." National Academies of Sciences, Engineering, and Medicine. 2018. Use of Automated Machine Guidance within the Transportation Industry. Washington, DC: The National Academies Press. doi: 10.17226/25084.
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Suggested Citation:"Chapter 8: Impact of AMG on Earthwork Quantities." National Academies of Sciences, Engineering, and Medicine. 2018. Use of Automated Machine Guidance within the Transportation Industry. Washington, DC: The National Academies Press. doi: 10.17226/25084.
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Suggested Citation:"Chapter 8: Impact of AMG on Earthwork Quantities." National Academies of Sciences, Engineering, and Medicine. 2018. Use of Automated Machine Guidance within the Transportation Industry. Washington, DC: The National Academies Press. doi: 10.17226/25084.
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Suggested Citation:"Chapter 8: Impact of AMG on Earthwork Quantities." National Academies of Sciences, Engineering, and Medicine. 2018. Use of Automated Machine Guidance within the Transportation Industry. Washington, DC: The National Academies Press. doi: 10.17226/25084.
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Suggested Citation:"Chapter 8: Impact of AMG on Earthwork Quantities." National Academies of Sciences, Engineering, and Medicine. 2018. Use of Automated Machine Guidance within the Transportation Industry. Washington, DC: The National Academies Press. doi: 10.17226/25084.
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Suggested Citation:"Chapter 8: Impact of AMG on Earthwork Quantities." National Academies of Sciences, Engineering, and Medicine. 2018. Use of Automated Machine Guidance within the Transportation Industry. Washington, DC: The National Academies Press. doi: 10.17226/25084.
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Suggested Citation:"Chapter 8: Impact of AMG on Earthwork Quantities." National Academies of Sciences, Engineering, and Medicine. 2018. Use of Automated Machine Guidance within the Transportation Industry. Washington, DC: The National Academies Press. doi: 10.17226/25084.
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Suggested Citation:"Chapter 8: Impact of AMG on Earthwork Quantities." National Academies of Sciences, Engineering, and Medicine. 2018. Use of Automated Machine Guidance within the Transportation Industry. Washington, DC: The National Academies Press. doi: 10.17226/25084.
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NCHRP Project 10-77 81 CHAPTER 8: IMPACT OF AMG ON EARTHWORK QUANTITIES BACKGROUND Proper use of digital information for AMG will likely result in less confusion and more accuracy than traditional methods of earthwork pay item quantification and payment. Earthwork pay quantification from AMG must include mechanisms that all parties to the contract (both the agency-owner and the contractor) can trust. The efficient use of digital information in AMG applications typically involves creation of a DTM in the planning functional area, which is then passed to the design functional area for creation of a separate 3D model. This facilitates efficient computation and measurement of earthwork quantities for use in the procurement functional area (bidding). Finally, the construction functional area is concerned with verification of project as-built quantities. The two primary areas of implementation to address are processes that are either internal to transportation agencies or external. Internal issues will be found as the design is transferred between the planning, design, procurement, and construction functional areas to leverage these modern technologies for improved efficiencies in project delivery. External issues lie with agencies passing the digital data (along with conventional paper drawings) to the construction contractor for building the AMG project and mutually agreed upon quantification of earthwork pay item quantities in unit price contracts. Hannon and Sulbaran (2008) found that the technology behind AMG is maturing, but, from an application standpoint exists in “information silos,” or with gaps between the agency functional areas. Hannon and Sulbaran state: Besides software application interoperability in general, the primary gap in the realization of TIM delivery, according to our case studies, is that currently the methodology begins to atrophy during and after the construction (project) lifecycle stage. That is to say, we found no evidence of a TIM model being utilized beyond the Procurement lifecycle stage. This phenomenon was reinforced by the literature review pertaining to BIM project delivery. In none of our findings did we find extensions of the data in the successive project lifecycle stages or functional areas. The exception is Florida DOT which utilizes datasets in file structures burned onto CD-ROMs throughout all of the lifecycle stages, but this is not the true TIM paradigm. SURVEY RESPONSES AND PREVIOUS CASE STUDIES Survey Responses on the Use of DTMs for Estimation Figure 8-1 presents responses from contractors on survey questions pertaining to their use of DTMs for estimating, collection of earthwork quantities, and payment. These responses indicate that most of the responding contractors use DTMs for estimating quantities, means and methods, constructability, quantity of the progress of work, and payment.

NCHRP Project 10-77 82 No Ye s ( Qu an titi es ) Ye s ( Me an s a nd M eth od s) Ye s ( Co ns tru cta bil ity ) Ot he r Fr eq ue nc y 0 5 10 15 20 25 30 Ye s No Ot he r No A ns we r No t C om ple ted Fr eq ue nc y 0 10 20 30 40 50 60 Ye s No Ot he r No A ns we r No t C om ple ted Fr eq ue nc y 0 10 20 30 40 50 60 Question: Do contractors utilize DTMs for estimating? Question: Do contractors utilize DTMs for collection of earthwork quantities? Question: Do contractors utilize DTMs for payment? Note: Some respondents specifically answered as "No Answer" whereas respondents who did not have a response and skipped the question entirely were marked as "Not completed". Figure 8-1. Survey Responses by Contractors on the Use of DTMs Impact of AMG on Productivity Gain and Cost Savings Figure 8-2 presents responses from contractors and vendors on the impact of AMG on productivity gain and project cost savings. Most of the equipment vendors indicated potential productivity gain of about 40% and potential cost savings of about 25 to 40% using AMG. On the other hand, most of the contractors indicated potential productivity gain of about 10 to 25% and potential cost savings of about 10 to 25% using AMG. Productivity gain and cost savings reported in the literature on earthwork construction projects using AMG is also presented in Figure 8-2 (Jonasson et al., 2002; Aðalsteinsson, 2008; Forrestel, 2007; Higgins, 2009; Caterpillar, 2006). Jonasson et al. (2002) reported productivity gain and cost savings information for a fine grading project using Caterpillar 140 H motorgrader with different position measurement technologies (i.e., ultrasonic, 2D and 3D lasers, and GPS). The productivity gain ranged from about 20 to 100% and cost savings ranged from about 15 to 40%, depending on the position measurement technology used. The cost savings were due to a reduction in surveying support and grade checking, an increase in operational efficiency, and a decrease in number of passes. The Jonasson study indicated that the 3D laser systems required a direct line of sight to the equipment while the GPS systems did not. This resulted in a minor increase in fleet productivity and a decrease in unit cost using GPS guidance systems over 3D laser systems.

NCHRP Project 10-77 83 Productivity Gain Using AMG (%) 10 20 30 40 50 Fr eq ue nc y 0 2 4 6 8 10 12 Contractors Vendors Cost Savings Using AMG (%) 10 20 30 40 50 Fr eq ue nc y 0 2 4 6 8 10 12 Contractors Vendors >50 Ultrasonic1 2D Laser1 3D Laser1/ GPS1 (89 to 101%) Ultrasonic1 2D Laser1 3D Laser1 GPS1 GPS2 Notes: 1Fine-grading using CAT 140H motor grader (Jonasson et al., 2002) 2Trench excavation using CAT 330DL hydraulic excavator (Aðalsteinsson, 2008) 3Earth moving and fine grading (general values; not project specific) (Forrestel, 2007) 4Earth moving and fine grading project - Port of Brisbane (Higgins, 2009) Notes: 1Fine-grading using CAT 140H motor grader - Overall unit cost (Jonasson et al., 2002) 2Earth moving and fine grading project - Port of Brisbane (overal cost savings) (Higgins, 2009) GPS3GPS 4 GPS2 GPS7 (265%) 5Bulk earth moving and subgrade fine grading using CAT D6N dozer (gain in the number of passes; Caterpillar, 2006) 6Bulk earth moving using CAT 330D excavator (Caterpillar, 2006) 7Base course fine grading using CAT 140H motorgrader (gain the number of passes; Caterpillar, 2006) GPS6 GPS3 3Bulk earth moving using CAT 330D excavator - fuel cost savings (Caterpillar, 2006) 4Bulk earth moving and fine grading using CAT D6N dozer - fuel cost savings (Caterpillar, 2006) 5Fine grading using 140H motorgrader - fuel cost savings (Caterpillar, 2006) GPS4 GPS5 (68%) >50 GPS5 (107%) Figure 8-2. Survey Responses by Contractors and Vendors and Productivity Gain and Potential Cost Savings using AMG, and Data obtained from Field Case Studies (Source: Vennapusa et al. 2015; ©2015 Iowa State University; used with permission)

NCHRP Project 10-77 84 Aðalsteinsson (2008) reported results from a field demonstration project conducted using a CAT 330DL excavator to excavate a trench with 1650 cubic yards of sandy gravel material. The field study involved comparing excavation productivity and cost differences between the conventional approach and AMG approach. The AMG approach involved performing the excavation using a digital model loaded into the on-board display software and GPS-based position measurement system on the 330DL excavator. The AMG approach showed a productivity gain of about 25%. Caterpillar (2006) reported results from a field demonstration project conducted in Spain by constructing two 80 m identical roads: one road with AMG on construction equipment and the other with similar equipment but using conventional methods and no AMG. A CAT 330D excavator and CAT D6N dozer were used for bulk earth moving. The CAT D6N dozer was used for subgrade fine grading, and a CAT 140H motor grader was used for subbase fine grading work. An overall productivity increase of about 101%, fuel cost savings of about 43%, and increased consistencies in grade tolerances were reported for this project. Productivity gain and fuel cost savings for earth moving and fine grading work are shown separately in Figure 7.2. The results from these field case studies and survey responses indicate that the productivity gain and cost savings using AMG on earthwork projects can vary significantly (with productivity gains in the range of 5% to 270% and cost savings in the range of 10% to 70%). This variation is most likely because of various contributing factors, such as project conditions, materials, application, equipment used, position measurement technologies used, and operator experience. Cable et al. (2009) recently reported results from a portland cement concrete (PCC) paving project in Iowa, where the stringless AMG approach was compared to the conventional string line approach. Table 7-1 presents a summary of the pavement quality test results obtained from the project. Table 8-1. Comparison of Traditional String Line Control and Stringless AMG Approaches for PCC Paving (from Cable et al., 2009) Property Approach Measurement California profilograph index (CPI) Stringless 324.3 to 443.0 mm/km (20.6 to 26.8 in/mile) String line 22.7 to 124 mm/km (1.44 to 7.86 in/mile) Pavement thickness (edges) Stringless Standard deviation = 0.43 in (11 mm) String line Standard deviation = 0.67 in (17 mm) Pavement thickness (center) Stringless Standard deviation = 0.28 in (7 mm) String line Standard deviation = 0.47 in (12 mm) Pavement surface elevation deviation from design (north edge) Stringless Average = -0.6 in (-15 mm) String line Average = -0.5 in (-12 mm) Pavement surface elevation deviation from design (south edge) Stringless Average = -0.4 ft (-9 mm) String line Average = -0.1 ft (-4 mm) The pavement ride quality measurements (California profilograph index, CPI) indicated that the ride quality is somewhat better using the conventional string line approach compared to the stringless approach. Also, the conventional string line approach produced better surface elevation conformance to design than the stringless approach. However, the standard deviations of the pavement thickness were lower with the stringless approach than with the string line approach, which is an indication of better thickness control/accuracy with the stringless approach. While results from this project showed certain limitations with regards to the pavement ride quality and deviations in the pavement surface elevations using the stringless approach, the lower standard deviations of pavement thickness are certainly encouraging. Elevation data of the underlying subbase layer and its contribution to the pavement ride quality or deviations in the surface elevations were not studied. However, it appears that it is an important aspect and must be further investigated to properly understand the impact of AMG on the overall quality, productivity, and cost for paving projects.

NCHRP Project 10-77 85 Galbraith (2009) reported cost savings of about $10,000 per production mile on a stringless PCC paving project in North Carolina, where Gomaco PS2600 spreaders, controlled by RTK GPS, and a Gomaco GHP 2800 slip form paver, controlled by robotic prisms, were used. The cost savings are attributed to avoided stakeout costs (about $1,200 per day with 1500 linear ft per day production). Cost Savings Model One of the most significant benefits of using AMG for earthwork applications is the cost savings, which is a result of: • Gain in productivity, • Material quantity savings (reduction in overages), and • Reduction in survey crew time and efforts. Calculations involved in determining costs involved in construction operations, material quantities, and survey crew cost, on a typical subbase fine grading project, are provided below (Schaufelberger, 1999). Calculations Productivity can be estimated following a conventional area estimate approach using equation 8- 1. NCT EAhfttyProductivi Area × × =)/( 2 (8-1) where A is the area graded per cycle (ft2) or length per cycle x average road way width, E is the operational efficiency (min/h), CT is the cycle time (min) determined using equation 8-2, and N is the number of passes required. T T F F V D V D CT += (8-2) where DF is the distance the grader travels when moving forward (ft), VF is the average forward speed of the grader (ft/min), DT is the distance the grader travels when turning (ft), and VT is the average turning speed of the grader (ft/min). Productivity in linear miles can be estimated using equation 8-3. 5280 )( WtyProductivimilestyProductivi AreasLinearMile × = (8-3) where W is the average road way width. The cost of operation can be estimated using equation 7-4. tFleettyProductiviCostProduction sLinearMile cos($) ×= (8-4)

NCHRP Project 10-77 86 Survey crew cost can be estimated using equation 8-5. HourlyCostPT($)SurveyCost ××= (8-5) where T is the time (number of hours) required for stakeout and surveying work per mile, P is the number of persons required, and Hourly Cost is the cost per person ($ per hour). Material quantities can be estimated using equation 8-6. 2240 UnitCostoverageHWL($)stMaterialCo ××γ×××= (8-6) where L is the length of the project, W is the average roadway width, H is the thickness of the subbase layer; γ is the unit weight of the material (pcf), overage is the extra material acquired, and unit cost is the unit cost of material ($ per ton). Assumptions Following are the assumptions and values used in the calculations to estimate the overall cost savings on a subbase fine grading project using AMG as a function of productivity gain (with reduction in number of passes, N), and material savings (or reduction in material overage). • Project length, L = 10 miles • Average width of the road way, W = 40 ft • Subbase layer thickness, H = 1 ft • Grading distance per cycle, CF = 1000 ft • Cycle time, CT (estimated based on 1000 ft grading distance at an average speed of 328 ft/min and 50 ft turning distance at an average speed of 328 ft/min) = 3.2 min • Unit weight of the material, γ = 145 pcf • Unit cost of the material = $12/ton • Number of hours required for stake and surveying work per mile (for conventional method) = 30 hours • Number of hours required for stake and surveying work per mile (conventional method), T = 30 hours • Number of hours required for stake and surveying work per mile (AMG method), T = 5 hours • Number of persons required for surveying work (conventional method), P = 3 • Number of persons required for surveying work (AMG method), P = 1 • Hourly cost of survey crew = $35/hour • Fleet cost (conventional method) = $570.75/hour (Jonasson et al., 2002) • Fleet cost (AMG method) = $694.35/hour (using GPS; Jonasson et al., 2002) • Material overage reduction = overage using conventional method – overage using AMG method Estimated Cost Savings Figure 8-3 shows the estimated cost savings using AMG over the conventional method, using the formula and assumptions described above, as a function of productivity gain and material overage

NCHRP Project 10-77 87 reduction. The trends in these estimations indicate that the contribution of the material overage reduction factor is significant, while the productivity gain is somewhat significant to the overall cost savings. Productivity Gain (%) 0 20 40 60 80 100 P er ce nt C os t S av in gs (% ) 0 2 4 6 8 10 0.09 ft 0.08 ft 0.06 ft 0.04 ft 0.02 ft 0.00 ft Overage Reductions $$ from reduced survey crew servcies Figure 8-3. Estimated Percent Cost Savings on a Subbase Fine Grading Project using AMG EARTHWORK QUANTITY COMPUTATION AND MEASUREMENT Accuracy of DTMs Recommendations on best practices for development of DTMs are discussed in detail in Chapter 7. This section discusses the accuracy of DTMs as they are influenced by the number of data points and the interpolation methods used because the accuracy of DTMs directly influences quantity estimations and the AMG process. Survey results, reported in Chapter 4, indicated that a majority (> 70%) of contractors, software/hardware vendors, and agencies who responded believe that the number of elevation data points used in creating the DTM is a principal factor in the accuracy of the DTM. Evaluating the accuracy of DTMs by comparing them to the actual surface is a challenging and expensive task. Some previous studies (such as Acharya et al., 2000 and Meneses et al., 2005) have attempted to evaluate the accuracy of DTMs. Meneses et al. (2005) compared the actual and interpolated surface elevations and volume characteristics using 5% to 100% point data densities. They concluded that the estimated volume characteristics are sensitive to the point data density, while the height errors are not. For the terrain features presented by Meneses, minimal increases in volume accuracies were noted with point densities above 60%. Acharya et al. (2000) used digital photogrammetric methods with aerial triangulation techniques to establish pass, tie, and check points, using known ground control points (established using GPS). These methods provided high density data with less time consumption, compared to using just GPS surveying methods, and with greater accuracy, compared to analog photogrammetric methods. Various interpolation methods are available in the literature for generating contour grid data for DTMs. A brief description of these commonly-used methods follows: • Inverse distance to power

NCHRP Project 10-77 88 • Kriging • Local polynomial • Minimum curvature • Nearest neighbor • TIN Inverse distance to power is a multivariate, weighted average interpolation method (Davis, 1986). The weights are assigned such that the influence of one point relative to another declines with the distance from a grid node. Weighting is assigned using a weighting power that controls how the weighting factors drop off as distance from a grid node increases. As the power value increases, the grid node value approaches the value of the nearest point. Kriging is a robust interpolation method that uses the spatial features of the data to determine weighting factors, through selection of an appropriate geostatistical semivariogram model (Cressie, 1990). This procedure requires experience with geostatistical semivariogram modeling, but generally produces results with comparatively better accuracy than other interpolation methods. Local polynomial regression assigns values to grid nodes by using weighted least squares fit, with data within the grid node search eclipse (Myers, 1990). Minimum curvature uses an algorithm that assumes a smooth elastic-like membrane in interpolating the surface. The interpolation is not considered exact, but is close to exact, and it generates a surface with a curvature as small as possible. The algorithm follows a simple linear regression fit and the extraction of residuals are then interpolated and added to the regression surface data. The process is quite complex and involves an iterative procedure (Briggs, 1974). Nearest neighbor assigns the value of the nearest point to each grid node. This method does not interpolate the data between the data points. TIN produces a surface using a series of contiguous and non-overlapping triangles, constructed using the known elevation data points (Lee and Schachter 1980). This method is most commonly used for transportation applications. There are many algorithms for generating surfaces using the TIN method, and one of the most commonly used algorithms is the Delaunay triangulation. Figure 8-3 illustrates the influence of the number of data points and the type of interpolation method on DTM accuracy using results from a data set obtained from a sloping terrain (shown in Figure 8-4). Figure 8-3 shows elevation data points obtained over an area of about 540 m2 (5810 ft2). To study the influence of the number of data points, three different data sets, with 78, 38, and 11 data points, are considered in the analysis. DTMs generated using the six different interpolation methods described above are presented in Figure 8-5, 8-6, and 8-7 (for the 78, 38, and 11 data points, respectively).

NCHRP Project 10-77 89 X (m) 2043 2046 2049 Y (m ) 220 230 240 250 260 270 280 X (m) 2043 2046 2049 220 230 240 250 260 270 280 X (m) 2043 2046 2049 220 230 240 250 260 270 280 (a) (b) (c) Figure 8-4. Elevation Data points for Developing DTM over a 540 m2 Area: (a) 78 Data Points; (b) 38 Data Points; and (c) 11 Data Points Figure 8-5. Picture of the Area with Elevation Data

NCHRP Project 10-77 90 Figure 8-6. DTMs of a 540m2 Area using 78 Elevation Data Points using Different Interpolation Methods: (a) Inverse Distance to a Power; (b) Kriging; (c) Local Polynomial; (d) Minimum Curvature; (e) Nearest Neighbor; (f) TIN Figure 8-7. DTMs of a 540m2 Area using 38 Elevation Data Points using Different Interpolation Methods: (a) Inverse Distance to a Power; (b) Kriging; (c) Local Polynomial; (d) Minimum Curvature; (e) Nearest Neighbor; (f) TIN

NCHRP Project 10-77 91 Figure 8-8. DTMs of a 540m2 Area using 11 Elevation Data Points using Different Interpolation Methods: (a) Inverse Distance to a Power; (b) Kriging; (c) Local Polynomial; (d) Minimum Curvature; (e) Nearest Neighbor; (f) TIN (Source: Vennapusa et al. 2015; ©2015 Iowa State University; used with permission.) The accuracy of each DTM that used 78 data points was evaluated using a cross-validation technique. This technique involved taking out a known data point from the data set, estimating the point using the model, and comparing the estimated value with the actual one. This process was repeated for all 78 data points. An absolute mean error (calculated as the average of absolute value of the difference between the actual and the estimate value) was then calculated for each interpolation method, as summarized in Table 8-2. Table 8-2. Absolute Mean Error of Estimated Elevation Data Based on Cross-Validation Process using Different Interpolation Methods Data Interpolation Method Estimated Elevation Absolute Mean Error (mm) Inverse distance to power 100 Kriging 20 Local polynomial 70 Minimum curvature 50 Nearest neighbor 40 TIN 30 For this data set, results indicated that the Kriging method is the most accurate method with 0.02 m (0.06 ft) absolute mean error. The TIN method showed a slightly higher absolute mean value of 0.03 m (0.10 ft). The grid generated using the Kriging method with 78 data points was then considered as a “true” representative surface, and it was used as a comparison to the grid data generated using the other interpolation methods, as summarized in Table 7-3.

NCHRP Project 10-77 92 Table 8-3. Absolute Mean Error of Estimated Elevation Data by Comparing Kriged DTM with 79 points with Different Interpolation Methods Estimated Elevation Absolute Mean Error (m) Data Interpolation Method 79 Data Points 38 Data Points 11 Data Points Inverse distance to power 0.06 0.10 0.11 Kriging 0.00 0.02 0.05 Local polynomial 0.06 0.07 0.07 Minimum curvature 0.03 0.04 0.09 Nearest neighbor 0.06 0.12 0.24 TIN 0.01 0.04 0.06 The Kriging method produced absolute mean error of 0.02 (0.06ft) using 38 data points and 0.05 m (0.16 ft) using 11 data points. The TIN method produced slightly higher absolute mean error values. Minimum curvature, local polynomial, and inverse distance to power methods produced greater absolute mean error values, compared to the TIN method. The nearest neighbor method could not replicate the surface terrain, as it doesn’t interpolate the data, which is clearly a limitation of the method. It is extremely important that existing surfaces are portrayed as accurately as possible, so the model can be passed ahead to the design, estimation, bidding, and construction phases of the project with high fidelity. A proper understanding of the factors that influence the accuracy of the DTM (as described above and in Chapter 7) is important to understand and must be addressed during the model development phase. The limitations with these approaches, however, are the possibility of higher up-front costs for more data collection, software, and highly-trained personnel, and the possible inability to make gut-level checks for data collection errors. Some survey respondents reported the following costs involved with DTM development (see Appendix D): • $2,500/lane mile +/- $150 per lane mile • $800-1,000 per mile, based on $80/hour bill rate +/- $150 per lane mile • ± $750/lane mile and ± $50/acre • Typically, 10 hours to set up a 10 acre • Cost per runway (1.5 miles of project) varies from $10,000-$25,000, depending on complexity Computation of Earthwork Quantities Earthwork quantities are traditionally computed using average-end-area method, which is based on averaging the areas of two consecutive cross-sections and multiplying the average by the distance between them (Burch, 2007). The total sum is calculated by adding the quantities determined from each consecutive cross-section. Using DTM, the surface-surface method can be used to compute quantities, by overlapping the existing terrain and the design DTM surfaces. The U.S. Army Corps of Engineers (2002) provides a detailed explanation of the surface-surface quantity estimation method using TIN surfaces. Many software applications (including Bentley and Autodesk) now have the capability to easily compute quantities using the surface-surface method. The accuracy of the generated DTM, as described above, plays a significant role in the estimated earthwork quantities. Another key factor that contributes to the overall quantity estimation is the soil shrink-swell factors, which are dependent on the soil type, so they must be selected appropriately (Burch, 2007). Vanderohe et al. (2010) reported that differences between average-end-area and surface-surface increases as the cross-section intervals increase, although the relationship is not linear. As the cross-

NCHRP Project 10-77 93 section intervals decrease, the computations become theoretically the same. The differences are observed to be as great as 5% when 100 ft cross-section intervals are used with the average-end-area method. Such differences can contribute to significant cost discrepancies for large projects. The advantage of using DTMs is that earthwork quantities can be computed “on the fly,” as the model is being developed, and during construction. Various layers and volumes that represent various bid items and various costs can be collected and categorized during the design process. Designed surfaces are accurately portrayed and can be passed ahead in the AMG process with high fidelity. The limitations, though, include potentially higher up-front costs for software, hardware, and highly-trained personnel, and the possible inability to make gut-level checks for some types of design errors. Downstream personnel may be critical of design personnel for alternative designs that were not used and documented in unused parts of the model. Designers may consider inspection of the details of the design process by downstream personnel to be too invasive of their professional autonomy. Model Enhancement for Construction Purposes Model enhancement might be necessary during the development process for certain aspects, such as providing offsets between pavements and subgrades, delineating areas where equipment operation is excluded, and correcting inconsistencies that are not problematic for design models but are for AMG. The benefits of this work phase are that the constructor may discover possible design improvements or design errors in the model, which can end up saving time and money during construction. The constructor may develop a better understanding of how to construct the project as the design model is enhanced. The constructor could improve construction productivity and safety by adding exclusion zones for equipment and methods to track equipment usage during construction. The liabilities with this work phase are potentially higher up-front costs for equipment and highly-trained personnel, the possible inability to make gut-level checks for design errors or construction enhancement errors, and the possibility of passing undetected errors from the previous process to the next one. Model Conversion to QA/QC Format QA/QC personnel can potentially use DTM and the final design model to automatically locate test locations and display results. Elevations of existing surfaces can be obtained quickly and modeled in 3D to estimate current earthwork and pavement volumes or tonnages for partial payments. Quality information is processed along with volume information to ensure that partial payments are made for earthwork or pavement that meets quality requirements. However, liabilities, once again, are potentially higher up-front costs and the possible inability to make gut-level checks for data collection errors. If a proper data collection and documentation strategy is not developed, QA/QC personnel could also be overwhelmed by data overload and data processing. IMPACT ON EARTHWORKS SUMMARY • Proper use of digital information for AMG will likely result in less confusion and more accuracy than traditional methods of earthwork pay item quantification and payment. Earthwork pay quantification from AMG must include mechanisms that all parties to the contract (both the agency-owner and the contractor) can trust. • Many software applications now have the capability to easily compute quantities using the surface-surface method. The accuracy of the generated DTM plays a significant role in the estimated earthwork quantities. Another key factor that contributes to the overall quantity estimation is the soil shrink-swell factors. • Model enhancement might be necessary during the development process for certain aspects, such as providing offsets between pavements and subgrades, delineating areas where equipment operation is excluded, and correcting inconsistencies that are not problematic for

NCHRP Project 10-77 94 design models but are for AMG. • Most of the equipment vendors indicated potential productivity gain of about 40% and potential cost savings of about 25 to 40% using AMG. Contractors indicated potential productivity gain of about 10 to 25% and potential cost savings of about 10 to 25% using AMG. The results from detailed case studies described in the literature and survey responses indicate that the productivity gain and cost savings using AMG on earthwork projects can vary significantly (with productivity gains in the range of 5% to 270% and cost savings in the range of 10% to 70%). This variation is most likely because of various contributing factors, such as project conditions, materials, application, equipment used, position measurement technologies used, and operator experience. • It is extremely important that existing surfaces are portrayed as accurately as possible, so the model can be passed ahead to the design, estimation, bidding, and construction phases of the project with high fidelity. A proper understanding of the factors that influence the accuracy of the DTM is important to understand and must be addressed during the model development phase. • Survey results, reported in Chapter 3, indicated that a majority (> 70%) of contractors, software/hardware vendors, and agencies who responded believe that the number of elevation data points used in creating the DTM is a key factor in the accuracy of the DTM. Reference Vennapusa, P. K. R., D. J. White, and C. T. Jahren. 2015. Impacts of Automated Machine Guidance on Earthwork Operations. Proceedings of the 2015 Conference on Autonomous and Robotic Construction of Infrastructure. Center for Earthworks Engineering Research at Iowa State University, Ames, IA. pp. 207– 216.

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TRB's National Cooperative Highway Research Program (NCHRP) Web-Only Document 250: Use of Automated Machine Guidance within the Transportation Industry studies automated machine guidance (AMG) implementation barriers and develop strategies for effective implementation of AMG technology in construction operations. AMG links design software with construction equipment to direct the operations of construction machinery with a high level of precision, and improve the speed and accuracy of the construction process. AMG technology may improve the overall quality, safety, and efficiency of transportation project construction.

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