Estimation of Water Stagnation in Asphalt-Overlaid Bridges Using Ground-Penetrating Radar

Lee, Junhwa; Choi, Jinwoong; Shin, Yooseong; Sim, Sung-Han

doi:https://doi.org/10.1155/2023/7280555

Structural Control and Health Monitoring

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Abstract Introduction Conclusions Appendix Data Availability Conflicts of Interest Acknowledgments References Copyright Related Articles

Research Article | Open Access

Volume 2023 | Article ID 7280555 | https://doi.org/10.1155/2023/7280555

Estimation of Water Stagnation in Asphalt-Overlaid Bridges Using Ground-Penetrating Radar

Junhwa Lee,^1,2Jinwoong Choi,³Yooseong Shin,³and Sung-Han Sim¹

Academic Editor: Francesc Pozo

Received11 Jan 2023

Revised24 Mar 2023

Accepted31 Mar 2023

Published22 Apr 2023

Abstract

Stagnant water in asphalt-overlaid bridge decks is a primary cause of deterioration. Rainwater seeping through the asphalt layer stagnates on waterproofing membranes of the bridge deck, consequently degrading the asphalt pavement and the underlying concrete deck. Thus, identifying ponding regions under pavements potentially containing water can facilitate the prognostic maintenance of bridge decks. This study proposes a framework to estimate the subsurface ponding zone in bridge decks using ground-penetrating radar (GPR). The depth distribution of the nonpermeable layer in the subsurface of the bridge is extracted (depth map) from the GPR C-scan using a conventional thickness evaluation method and used to build a bathymetric dendrogram to model subsurface water flows. The subsurface ponding zone can be identified by considering drainage on the bathymetric dendrogram. The proposed framework is demonstrated using an in-service bridge in Korea. The estimated subsurface ponding zone is compared with damage locations of concrete observed after hydrodemolition.

1. Introduction

Asphalt-overlaid bridges are essential components of road networks in modern countries. Asphalt overlays offer skid resistance, noise mitigation, short curing time, and inexpensive construction; thus, 94% of highways in the United States are surfaced with asphalts [1]. Despite these benefits, asphalt-overlaid bridges are prone to defects, mostly in the form of potholes [2, 3] owing to moisture-related damage [4–6]. Thus, effective maintenance of the asphalt pavement and the underlying bridge deck is desirable for bridge serviceability and safety.

Water stagnation in pavements is a primary cause of structural deterioration in asphalt-overlaid bridges. The standing water on the road surface is drained through scuppers on the shoulder or absorbed by the asphalt overlay to prevent vehicles from skidding. However, the standing water that infiltrates the asphalt pavement eventually stagnates in the nonpermeable subsurface layer, such as the waterproofing layer [7]. Consequently, the stagnant water under the pavement persistently deteriorates the asphalt overlay and the underlying concrete deck through freeze-thaw cycles, chloride ingress, carbonation, and corrosion of the reinforcement [8–10]. Therefore, stagnant water in the pavement and bridge deck should be properly handled for the prognostic health management of bridges.

Nondestructive testing (NDT) has been employed to detect and quantify stagnant water in bridges. Common NDT practices for civil engineering structures involve using electricity, stress waves, and electromagnetic (EM) waves. Electricity-based approaches, such as concrete resistive methods, utilize electrodes to measure the conductivity of pore solutions and degrees of saturation in concrete [11, 12]. In contrast, stress-wave methods (e.g., impact echo) exert pressure on the structure and subsequently analyze the reflected P- and S-waves to identify material properties and the existence of voids, which can aid in water detection within the structure [13–15]. EM waves are used to evaluate the distribution of water in the subsurface region via reflection [16, 17], direct waves [18, 19], and frequency domain analysis [20, 21]. Although NDT approaches have been shown to detect stagnant water in the subsurface, the amount of water varies depending on the moisture condition of the bridge. Following precipitation, the remaining water in the subsurface reduces over time, which renders determining the application of the NDT strategies and method of using the obtained information in bridge maintenance challenging. For consistent quantification of stagnant water regardless of weather conditions, subsurface regions where water can potentially stagnate needs to be identified rather than direct measurement of the water. Herein, the geometrically dented region in the subsurface layer can be regarded as a potential region of water stagnation. Although the geometric shape of the subsurface layer obtained by NDT can be employed to detect the potential region of water stagnation, existing studies have focused on the direct quantification of subsurface water.

Previous studies exhibit utilization of the ground-penetrating radar (GPR), an EM-wave method, in the subsurface survey to quantify the moisture content within the pavement layers. Abufares et al. [22] evaluated the performance of the GPR in predicting the moisture content in cold in-place recycling of asphalt concrete pavement by tracking the dielectric properties during curing. Cao and Al-Qadi [23] simulated the effect of the moisture content in asphalt concrete pavement to the dielectric constant, proposing a curve-fitted relationship between the moisture content and the resulting dielectric constant. Calhoon et al. [24] validated the capability of GPR in monitoring the seasonal moisture variation in the pavement. Here, the volumetric moisture content captured by the GPR showed good agreement with the Falling Weight Deflectometer, validating the utility of the GPR-based moisture content monitoring. These studies focused on detecting the distribution of the water within the heterogenous pavement materials, utilizing the nature of the dielectric constant of the subsurface medium that varies as per the moisture content. The stagnant water between the asphalt pavement and the underlying concrete can be obtained by monitoring the dielectric constants of the subsurface layers. However, the volume of the detected water depends on the current moisture condition of the asphalt, mostly related to recent precipitations. Thus, an approach that can determine locations of potential stagnant water would provide useful information for the maintenance purposes.

This study proposes a framework capable of efficiently identifying potential regions of water stagnation under a bridge pavement using GPR. Instead of directly measuring stagnant water, the proposed framework estimates the locations and volumes of stagnant water through an analysis of the geometric shape of the subsurface layers. In the proposed method, GPR is employed to probe the subsurface of the pavement and identify the depth distribution of the waterproofing layer. Subsequently, the measured depth distribution is investigated using a bathymetric dendrogram to identify the local regions surrounded by high elevations where water can be retained. Thereafter, the locations and volumes of the potential water stagnation regions are computed by considering the non-drained components in the bathymetric dendrogram. The proposed framework is experimentally validated using a highway bridge in the Republic of Korea.

2. Fundamentals of GPR

Modern GPR systems comprise radars mounted on a mobility system to scan the subsurface of bridges at multiple points. GPR systems consist of transmitter and receiver couples that emit and receive short-pulse EM waves, typically in the range of 0.4–2.6 GHz for structure surveys [11]. The time history of EM-wave reflection, referred to as GPR A-scan data, can be used to measure the radial distance to the underlying objects, as shown in Figure 1(a). The single A-scan data are insufficient for the localization of the subsurface structure; thus, an advanced subsurface survey is conducted by acquiring GPR A-scan data from multiple points using a mobility system. Multiple GPR A-scan data are merged to form two- and three-dimensional data, referred to as GPR B-scan and GPR C-scan, respectively, as shown in Figure 1(b). Considering three-point localization [25], a GPR C-scan can determine the three-dimensional location of objects, voids, and interlayers in the subsurface composition. Hence, GPR C-scan is widely adopted to scan three-dimensional subsurface layers to assess bridge pavement condition.

(a)

(b)

The thickness of a medium in the subsurface of a bridge can be evaluated using GPR and two-way travel methods. The amplitude of the GPR signal reflected from the material boundary is related to the dielectric constant as follows [26]:where A_ref and A_inc are the amplitudes of the reflected and incident signals, respectively, and ε_inc and ε_ref are the dielectric constants of the incident and reflected media, respectively. The GPR signal indicates a sharp change when crossing the material boundary owing to the difference between ε_inc and ε_ref. Consequently, the time duration between the top and bottom boundaries of a medium can be measured because of noticeable changes in the GPR data. The medium thickness D is related to as follows [26]:where C is the speed of the EM wave in an vacuum, which is approximately 3 × 10⁸ m/s, and ε_rel is the relative dielectric constant, defined as follows [27]:where ε_tar and ε₀ are the dielectric constants of the target medium and the vacuum, respectively. Note that the current value of ε_rel, which typically varies upon the amount of moisture in the medium, is determined by a calibration experiment in the field. Once is obtained from the GPR A-scan data, D can be identified for a target medium using equation (2) with a known ε_tar. This two-way travel method is employed in the proposed framework described in Section 3.

3. Proposed Framework for Estimation of Water Stagnation

This paper proposes a framework capable of efficiently localizing and quantifying water stagnation in the subsurface of asphalt-overlaid bridges using GPR. Stagnant water on the road surface infiltrates through the asphalt overlay and sits on the underlying nonpermeable layer, such as a waterproofing membrane or bridge deck, as illustrated in Figure 2. Locations of the water stagnation in the subsurface needs to be identified because they can be a source of persistent deterioration of structural integrity, as discussed in Section 1. Considering the geometric shape of the underlying layers measured by GPR, the proposed method facilitates the identification of the potential regions of stagnant water (hereafter denoted as the subsurface ponding zone) regardless of the moisture condition. This section describes the method of estimating the subsurface ponding zone from the GPR data.

The proposed framework comprises two steps, as shown in Figure 3. The depth distribution of the nonpermeable layer is computed from the GPR C-scan using the two-way travel method, which is discussed in Section 3.1. The subsurface ponding zone is computed by constructing a bathymetric dendrogram, which is described in Section 3.2.

3.1. Preprocessing of GPR Data

The asphalt thickness over the bridge can be determined using GPR C-scan data and a two-way travel method, as discussed in Section 2. The subsurface of the bridge generally comprises air-asphalt-concrete layers, as shown in Figure 2. Considering the ranges of the relative dielectric constant (i.e., ε_rel) for air, asphalt, and concrete as 1, 3–5, and 5–10, respectively [28, 29], the asphalt thickness can be evaluated from the GPR A-scan data using equation (2). Let x and y be the longitudinal and transversal coordinates on the bridge deck, respectively, as shown in Figure 2. The planar distribution of the asphalt thickness D(x,y) can be obtained via the application of the two-way travel method to the GPR C-scan.

The depth distribution of the underlying nonpermeable layer is computed using D(x,y) and the cross slope of the bridge. Assume that the upper surface of the asphalt layer is flat. Thus, uneven surfaces such as potholes are not considered in this study. The depth distribution of the underlying layer Z(x,y) under the asphalt overlay is calculated as follows:where θ is the angle of the cross slope, as shown in Figure 2; its value can be found in the bridge design. Z(x,y) is the depth distribution of the underlying nonpermeable layer, which is employed in the computation of the subsurface ponding zone in Section 3.2.

3.2. Computation of Subsurface Ponding Zone

The subsurface ponding zone is computed from the depth distribution Z(x,y) using a bathymetric dendrogram. The proposed computation procedure first builds a bathymetric dendrogram representing water flow in the pavement. Subsequently, the tree structure is used to determine the locations and depths of the subsurface ponding zone. Consider an arbitrary depth distribution Z(x,y) in Figure 4(a) with plane C, which slices Z(x,y) horizontally, as shown in Figure 4(b). Cutting plane C is initially placed at the highest level of Z(x,y), denoted by Z_max (Figure 4(b)). Subsequently lowered by a user-defined depth interval ∆h at each step, plane C is placed at a height of Z_max − (j − 1)∙∆h for the j^th step. As illustrated in Figure 4(b), a binary image I_j(x,y) is computed using the cutting plane C at the j^th step with the thresholding equation expressed as

(a)

(b)

Let R_j(k) be the k^th bounded region within I_j(x,y) with values of 1 (Figure 4(b)), and N_j(k) be the node for the bathymetric dendrogram having the planar coordinates (x,y) of the region R_j(k) as the node value. Consider a node at the j^th step, N_j(k), that adopts N_j+1(l) as its child node if a common planar coordinate (x, y) exists, as illustrated in Figure 4(b). Then, the bathymetric dendrogram can be established by linking all nodes to the corresponding parent node. The final bathymetric dendrogram can describe the connection of the open regions (i.e., R_j(k)) from the top surface of the nonpermeable layer to the bottom. As the water contained in R_j(k) is naturally distributed to its child nodes in proportion to their area, the bathymetric dendrogram helps simulate the water flow channels. As such, for any given Z(x,y), a bathymetric dendrogram can be constructed by linking all R_j(k) for all j and k.

Once the bathymetric dendrogram is constructed, the subsurface ponding zone is computed by considering the drainage points. Because a region that includes a drainage point (see Figure 5(a)) cannot accommodate water, the corresponding node in the bathymetric dendrogram is marked as a drained node, as shown in Figure 5(b). The subsurface ponding zone is subsequently obtained by combining the regions corresponding to the nondrained nodes, as shown in Figure 5(c). Here, the subsurface ponding zone S(x,y), defined as the depth of stagnant water at (x,y), is computed as follows:where ∆x and ∆y are the longitudinal and transverse scanning intervals, respectively, and X_jk(x,y) is expressed as

(a)

(b)

(c)

The relevant pseudocode for the proposed algorithm is provided in Appendix A.

4. Field Demonstration

The proposed method is demonstrated using a highway bridge located in the Republic of Korea. The testbed is a continuous prestressed concrete (PSC) box-girder bridge with ten supporting piers spanning 550 and 12.14 m along the traffic direction and width, respectively, as shown in Figure 6. The asphalt thickness and the concrete cover are designed to be 80 mm. Each span of the bridge is designed to have three scuppers every 5, 25, and 45 m on the shoulder. Along with the scuppers, the expansion joints at both ends of the bridge are considered as the drainage points. The subsurface ponding zone of the testbed is computed using the proposed framework with GPR C-scan data and the design information. After the scan, the deck overlays are hydrodemolished for maintenance. This section demonstrates the computation of the subsurface ponding zone of the testbed using the GPR C-scan, which is then compared with the structural damage assessed after hydrodemolition.

The condition of the bridge deck is examined using a GPR-mounted vehicle, as shown in Figure 7(a), which comprises four channels of 1 GHz horn antennas, a data acquisition system (SIR-30 from GSSI), and an odometer. Antennas are 45 cm away from the bridge surface emitting a 1 GHz EM wave with a 50 kHz repetition rate. The SIR-30 acquires GPR data with a time resolution of 0.029 ns by setting 1024 samples per 15 ns of scanning duration. Considering nearly 300 mm/ns of light speed in a vacuum, depth resolution is computed as 8.7 mm. Forty sets of GPR A-scan data are acquired per meter; therefore, the longitudinal resolution ∆x is 25 mm. Twenty scanning lines are considered for 12.14 m of the deck width as shown in Figure 7(b), resulting in the transverse resolution ∆y of 600 mm, approximately. Because of driving safety, GPR scanning is not conducted for the shoulder ranging from 300 to 350 m along the traffic direction where noticeable damage is observed.

(a)

(b)

The GPR C-scan acquired in the testbed is processed to identify the subsurface ponding zone, as discussed in Section 3. First, the asphalt thickness D(x,y) is computed, as shown in Figure 8(a), which is provided by the commercial GPR software RADAN [30]. Here, D(x,y) is obtained at the (x,y) position determined by the GPR hardware setup (∆x and ∆y are approximately 25 and 600 mm, respectively). The empty zone in D(x,y) (i.e., x = 300–350 m and y = 1.4–3.4 m) is a highly deteriorated region where the GPR scan is not performed because of safety issues. Next, the depth distribution Z(x,y) is computed, as shown in Figure 8(b), using equation (4) in conjunction with 2% of the cross slope that can be found in the bridge design. The cross slope aids in removing standing water from the bridge deck by flowing off to the shoulder where the scuppers are located. Third, a bathymetric dendrogram is constructed using Z(x,y) with a depth interval (∆h) of 1 mm as shown in Figure 8(c). The drained nodes in the bathymetric dendrogram are identified using the locations of the scuppers and the expansion joints. Finally, the subsurface ponding zone S(x,y) is determined from the bathymetric dendrogram using equation (6) as shown in Figure 8(d).

(a)

(b)

(c)

(d)

The subsurface ponding zone in Figure 8(d) represents potential regions of water stagnation. Water is expected to stagnate near the shoulder (i.e., near y = 0) owing to the cross slope. Although scuppers in the shoulder are distributed to collect standing water, the irregular elevation in the subsurface layer results in water stagnation. The red zones in Figure 8(d) represent the regions where a larger volume of water is contained than those in blue. Such regions are prone to deterioration owing to persistent carbonation, chloride ingress, freeze-thaw cycles, and corrosion of the reinforcement; therefore, the identified subsurface ponding zone can be utilized in a preventive maintenance scheme. Thus, the subsurface ponding zone can be used for the prognostic health management of bridges.

The shoulder pavement is hydrodemolished under regular pressure to repair damaged decks and pavements. The hydrodemolition removes the asphalt pavement and the deteriorated concrete of the bridge deck, revealing lurking structural damage as marked in brown in Figure 9. Although the water stagnation localized by the subsurface ponding zone only suggests the potential deterioration of the bridge deck and pavement, the damage locations in Figure 9 show a similar trend to that of the subsurface ponding zone in Figure 8(d).

Three regions on the shoulder—120–130, 235–245, and 400–410 m along the traffic direction (x) as shown in Figure 9 are selected for further investigation of the relationship between concrete damage and the subsurface ponding zone. The remaining concrete deck after the hydrodemolition and corresponding subsurface ponding zone are shown in Figure 10. The concrete deck in Region 1 is removed by hydrodemolition owing to deterioration as shown in Figure 10(a). However, water stagnation is not expected from the corresponding subsurface ponding zone. A possible reason for this discrepancy is the inaccurate estimation of Z(x,y). The proposed method assumes the upper surface of the asphalt layer is flat as discussed in Section 3; uneven asphalt surfaces can result in imprecise Z(x,y). In contrast, Regions 2 and 3 are consistent with the subsurface ponding zone, as shown in Figures 10(b) and 10(c), respectively. Therefore, the subsurface ponding zone can be used to identify the potential region of deterioration in the subsurface of a bridge without removing the pavement.

(a)

(b)

(c)

The field test exhibits the practical issues of the proposed framework in the maintenance of full-scale bridge. The bridge is scanned by the moving vehicle, which causes errors mostly in the form of amplitude error due to antenna height drift and Doppler frequency shift owing to vehicle vibration [31]. The influence of the amplitude error due to the shifting antenna height is subtle in the proposed method as the time picked at peaks is employed for the asphalt thickness evaluation. The Doppler frequency shift can lead to the incorrect evaluation of the asphalt thickness, while its influence is lessened using multiple measurement (i.e., 50 kHz repetition rate) assuming the white noise of vehicle vibrations. Furthermore, the increasing dielectric constant due to the moisture content leads to overestimation of the asphalt thickness if the relative dielectric constant for the dry asphalt is used. Note that reflection at the asphalt concrete interface takes longer in saturated pavement than dry one, resulting in the thickness overestimation. If the saturation is constant over the bridge deck, the subsurface ponding zone S(x,y) computed by the measured GPR C-scan becomes the scaled version of the true S(x,y). To correct the scale error, the exact dielectric constant needs to be computed in the field by the appropriate calibration model [23]. As such, the GPR scanning system needs to be carefully designed regarding the vibration and moisture-related issues.

5. Conclusions

This study proposed a framework for identifying the subsurface ponding zone for water stagnation in a bridge deck using GPR. Stagnant water in the subsurface of bridges persistently deteriorates the structural integrity through freeze-thaw cycles, chloride ingress, carbonation, or corrosion of the reinforcement. Thus, identifying potential region for water stagnation under the pavement aids in the preventive maintenance of the bridge deck. The proposed framework identified the subsurface ponding zone by constructing a bathymetric dendrogram from the GPR C-scan. The conventional asphalt thickness evaluation method was employed on the GPR C-scan to extract the depth distribution of the nonpermeable layer, which was used to build a bathymetric dendrogram that can efficiently simulate water flow channels. The subsurface ponding zone can be identified by introducing nondrained nodes in the bathymetric dendrogram. The proposed framework was demonstrated using an in-service PSC box-girder bridge. The testing vehicle, mounted with four channels of GPR, ran over the testbed to scan the subsurface of the bridge deck. Following the scan, the shoulder on the testbed was hydrodemolished to identify the structural damage under the pavement. Furthermore, the GPR C-scan data were used to compute the subsurface ponding zone, which was compared with the damage location identified after hydrodemolition. The field demonstration exhibited similar trends between the subsurface ponding zone from the GPR C-scan and the identified deteriorated region, providing an essential safety indicator for the prognostic health management of bridges. The proposed method needs further validation under different meteorological conditions to identify the coherence of the resulting subsurface ponding zone.

Appendix

A. Pseudocode

The pseudocode for computing the subsurface ponding zone S(x,y) from the depth distribution Z(x,y) is shown in Figure 11. The main code comprises three recursive functions, as Figure 11(a). Build_BD constructs a bathymetric dendrogram from Z(x,y), as shown in Figure 11(b). The input parameters in the pseudocode are the depth distribution, planar locations, depth interval, and index of the parent node, denoted by Z, xy_list, pivot, and dz, respectively. Build_BD recursively searches for child nodes using the flood fill algorithm [32, 33] denoted by bwconncomp. Drain_Water traverses BD to mark any node containing a drainage point, as shown in Figure 11(c). The input parameters are the bathymetric dendrogram, nodal index for the current node, and (x,y) coordinates of the drainage points, denoted by BD, current_idx, and drain_list, respectively. Finally, the subsurface ponding zone is computed by Build_S as shown in Figure 11(d). The input parameters are BD, current_idx, and unit volume (multiplication of x-interval, y-interval, and dz). Thus, the subsurface ponding zone can be efficiently obtained using Z(x,y) through recursive functions, as shown in Figure 11.

(a)

(b)

(c)

(d)

Data Availability

Some or all data, models, or codes generated or used during the study are proprietary or confidential in nature and may only be provided with restrictions.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Acknowledgments

This research was supported by a grant (23SMIP-A156887-04) from the Smart Construction Technology Development Program, funded by the Ministry of Land, Infrastructure, and Transport of the Korean Government.

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Copyright © 2023 Junhwa Lee et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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