Temperature-Based Evaluation and Monitoring Techniques …docs.trb.org/prp/16-2103.pdf ·...

Temperature-Based Evaluation and Monitoring Techniques for Long-Span Steel Bridges

Matthew Yarnold 1*, Brittany Murphy 2, Branko Glisic 3, and John Reilly 4

1 Tennessee Technological University Assistant Professor Department of Civil & Environmental Engineering P.O. Box 5015 Cookeville, TN 38505 (931) 372-3631 [email protected] *Corresponding author

2 Tennessee Technological University PhD Student Department of Civil & Environmental Engineering P.O. Box 5015 Cookeville, TN 38505 (423) 261-4514 [email protected]

3 Princeton University Assistant Professor Department of Civil and Environmental Engineering E330, EQuad Princeton, NJ 08544 (609) 258-8278 [email protected]

4 Princeton University PhD Student Department of Civil and Environmental Engineering Princeton, NJ 08544 (609) 258-8278 [email protected]

Word Count: Abstract: 158 Body: 6137 (including 250 words for each of the 9 figures) References: 1083 Total: 7378

Yarnold, Murphy, Glisic, and Reilly 2

ABSTRACT 1 This paper presents temperature-based (TB) evaluation and monitoring techniques for long-span 2 steel bridges. The underlying TB concept is based on the notion that temperature variations can 3 be treated as measureable loading for bridges and thus can be used to obtain a complete input-4 output relationship for structural evaluation. This is achieved through instrumentation of select 5 members and movement systems. Each location includes sensing for measurement of the 6 temperature (input) along with the corresponding strains, displacements, and/or rotations 7 (output). These measured relationships can identify a unique signature (or baseline) of the 8 structure. The TB signature can be utilized within the structural identification framework, termed 9 TBSI. This signature may also be leveraged for identification of unusual structural behaviors 10 within a structural health monitoring framework, termed TBSHM. In addition, the signature may 11 be utilized to supplement visual inspection, termed periodic temperature-based assessment 12 (PTBA). A general overview of each method is presented along with an application on a long-13 span steel tied-arch bridge. 14

INTRODUCTION 15 Long-span steel bridges are a critical infrastructure component for nearly all countries. They play 16 a substantial environmental, social, and economic role in their respective regions. The age of 17 many of these structures has grown with the average age of long-span bridges in the United 18 States at roughly 50 years [1]. This combined with the fact that many have become irreplaceable 19 due to political, historical, and financial reasons has pushed the focus from continual 20 replacement to indefinite preservation. Long-span bridges only make up a small percentage of 21 the United States bridge inventory; however, their importance to the nation is substantial. The 22 average daily traffic on a long-span bridge in the United States is roughly an order of magnitude 23 greater than conventional bridges [2]. The economic impacts of even temporary closure to these 24 structures are significant, not to mention the total replacement cost if not properly maintained. 25 The Blue Ribbon Panel estimates the loss of one of these structures as roughly $10 billion [3]. 26

Current practice for maintenance and preservation of long-span steel bridges places a 27 heavy focus on visual inspection which is slow and expensive due to the scale of these structures. 28 Many basic issues can be addressed with visual assessment techniques; however, there are 29 obvious flaws with the method. A more detailed assessment is sometimes performed using in-30 depth field testing. For a long-span steel bridge the primary approach used to date is ambient 31 vibration monitoring. While this approach has its benefits, there are many documented 32 drawbacks [4, 5] leaving researchers with the task to develop new methods. 33

The temperature-based (TB) concept presented in this paper aims to further progress the 34 state of structural evaluation and monitoring, and inspection techniques (Figure 1). At the heart 35 of the methodology is the idea that temperature variations can be treated as a measureable 36 “loading” of the structure and thus be used to obtain a complete input-output relationship. This is 37 achieved through instrumentation of both the critical members and movement mechanisms 38 (expansion bearings, joints, etc.). Each instrumentation location includes sensing for temperature 39 measurement (input) along with measurement of mechanical strains, displacements, and/or 40 rotations (output). These measured relationships associated with member forces and movement 41 mechanisms can identify a unique signature (or baseline) of the structure. This signature can be 42 utilized within the structural identification (St-Id) framework to determine various structural 43 parameters and evaluate the structural performance under different scenarios. This signature may 44 also be leveraged for identification of unusual structural behaviors within a structural health 45


monitoring (SHM) framework. In addition, the TB signature may be utilized to supplement 46 visual inspection. Fundamentally, the proposed TB concept has several innovative benefits 47 which include: 48

• measurable input and output response with a high signal-to-noise ratio; 49 • applicability to both linear and non-linear systems; 50 • relatively inexpensive equipment to capture the input-output relationship; 51 • negligible data storage requirements; 52 • minimal time synchronization requirements (only static data is needed); 53 • enhanced potential for St-Id and SHM as many structural parameters are highly sensitive 54

to temperature variations. 55

56 Figure 1: Temperature-Based Concept 57

TEMPERATURE-BASED CONCEPT ILLUSTRATIVE EXAMPLE 58 To illustrate the TB concept, a simple example is presented. Consider a simple beam with two 59 unknown parameters: a rotational spring (kR) and a longitudinal spring (kS) (Figure 2a). 60 Identification of these parameters can be achieved through the input-output temperature 61 relationship. For this example the structure is subjected to a top and bottom surface temperature 62 of T1 and T2, respectively with a linear temperature variation through the depth. Figure 2b 63 illustrates the deformed shape (T1>T2). Measurement of the temperature profile along with the 64 member strain allows for direct calculation of kR and kS using the fundamentals of 65 thermoelasticity. However, correlation of a numerical model with the measured results can be 66 utilized within the St-Id process to determine the parameters (kR and kS). These parameters can 67 also be tracked through time within a framework such as SHM to provide proactive maintenance 68 of the structure. Another alternative is to use the input-output information to supplement visual 69 inspection of the structure. 70

Member StrainsDisplacementsRotations

Structural System

Data Acquisition(input / output)

Identify Signature

Input Output

SHM InspectionSt-Id


71 Figure 2: Illustrative Simple Beam Model 72

LITERATURE REVIEW 73 Successful bridge assessment methods should be able to detect and characterize changes in the 74 structural condition and evaluate their impact on structural performance [6]. A thorough review 75 of current approaches leads to identification of new research avenues. Three main areas of prior 76 research are relevant to the temperature-based (TB) concept presented herein. This includes (a) 77 damage detection research through modal testing (b) structural identification (St-Id) of 78 constructed systems, and (c) structural health monitoring (SHM). A brief summary of each 79 research area is provided below followed by the direction of a new approach. 80

Current Approaches 81 (a) Damage Detection 82 A substantial subset of TB bridge research has been developed due to the emergence of damage 83 detection through modal testing. Damage detection techniques have aimed to exploit changes in 84 modal parameters to identify the extent and location of damage in structures. Early research 85 neglected environmental effects on modal parameters. However, the changes due to these effects 86 can often mask structural changes caused by damage [7]. One of the most important 87 environmental effects on modal parameters is temperature [8]. As a result, researchers have 88 focused on better understanding the influence that temperature has on dynamic properties, along 89 with finding ways to remove temperature effects to identify true damage [7-13]. While this area 90 of research has shown benefit in laboratory and field controlled settings, localization and reliable 91 quantification of damage in uncontrolled field applications has yet to be achieved [5]. 92

(b) Structural Identification (St-Id) 93 St-Id is a reliable framework through which sensing technology is employed for structural 94 assessment [14]. The St-Id paradigm was first introduced in engineering mechanics by Hart and 95 Yao [15] and in civil-structural engineering by Liu and Yao [16]. The six primary stages of St-Id 96 are illustrated in the inner circle of Figure 3. St-Id has recently fueled research in the structural 97 evaluation field due to documented discrepancies in the predicted versus measured responses of 98 constructed systems. Therefore, past studies have illustrated the significant improvement in 99 simulation reliability/accuracy that results from using field-measured response data to seed and 100 update analytical models [8, 17-20]. 101

When it comes to large-scale structures, there are limited viable techniques for obtaining 102 field-measured data to identify the structure. The most common measurement is ambient 103 vibration which can lead to obtaining the modal parameters of the structure (natural frequencies, 104

LkS

E,I,A,αkR

δU(a)

(b)T1

T2kR

kS


mode shapes, and damping). While ambient vibration has enjoyed significant attention over the 105 last several decades, it has many widely recognized challenges [4, 5]. The small signal-to-noise 106 ratio is a significant problem with vibration-based techniques due to the effect of varying 107 environmental conditions on the modal parameters, masking effects of structural changes [21]. 108 Additional weaknesses include the unknown nature of the inputs that are assumed as wide-109 banded white noise, predication on modal theory assumptions (linearity, stationary, etc.), and 110 significant data processing and storage requirements. Some of these shortcomings are gradually 111 being mitigated by advances in technology. However, others persist as they are associated with 112 fundamental assumptions of the method itself. Recently, researchers have compared TB data to 113 numerical models and have had limited success [22]. 114

(c) Structural Health Monitoring (SHM) 115 SHM is the practice of identifying and tracking performance of a structure by measured data and 116 analytical simulation. For successful SHM at least four criteria need to be considered: existence 117 (detection), localization, extent, and prognosis of structural impairment [23]. For SHM of long-118 span bridges all four criteria are yet to be reliably achieved. The most common SHM 119 experimental approach for long-span bridges is the use of ambient vibration monitoring [24-31]. 120 This methodology provides an overall characterization through tracking the modal parameters of 121 the structure. Long-term vibration monitoring suffers the same drawbacks as short-term vibration 122 testing which limits its capabilities (described in part (b) above). Distributed fiber optic strain 123 sensors have been recently applied at a larger scale on pipelines [32] and continuous-beam 124 bridge [33], however, their effectiveness on long-span bridges is still to be fully understood. 125

Recently TB research has been conducted within the SHM field. Some of the largest 126 responses for long-span bridges are due to temperature variations. As a result, many SHM 127 systems have included temperature based components [31, 34-37]. Researchers have primarily 128 utilized measured TB data for removal of temperature responses or direct monitoring of 129 temperature effects. Measurement of TB data for removal of temperature effects has previously 130 been conducted for tracking of other inputs (live load, wind load, etc.). One approach has been to 131 filter out the responses with various techniques [31]. Two dimensional (2D) regression models of 132 strain measurements have been implemented for outlier detection and intervention techniques 133 [36, 38] along with TB deflection data for filtering and identifying irreversible deformations 134 [39]. Direct monitoring of temperature responses has recently shown interest among researchers 135 which has primarily included tracking of bridge movements [22, 35, 37]. 136

Direction for a New Approach 137 The literature review leads to the conclusion that St-Id and SHM are the correct paradigms for 138 long-span bridge evaluation. However, additional experimental techniques are desired. The 139 primary sought after improvements include: 140

• Measurable input-output relationship: Current modal testing methods for long-span 141 bridges include output-only measurements that make assumptions about the nature of the 142 input (wide-banded white noise) limiting their use. 143

• No restriction of linearity: Many long-span bridges exhibit nonlinear behavior. Therefore, 144 experimental techniques should be flexible to account for this nonlinearity. 145

• Data storage and processing requirements: Bridge owners and engineers refrain from 146 using current techniques that require substantial data storage and processing. Large data 147 storage adds cost while complex processing requires a significant learning curve. 148

• Sensitivity: Current long-span bridge test methods are not sufficiently sensitive to detect, 149


locate, quantify, and prognosticate impairment on an operating structure. In fact, normal 150 environmental changes typically cause more deviation to the current metrics (e.g. 151 frequency or modal assurance criterion value changes) than substantial changes to the 152 structure. 153

Temperature-based (TB) testing methods address the shortcomings of the current techniques 154 listed above. While temperature-related research has been conducted over many years, it has 155 been given little attention as the main component of an experimental method. The focus has been 156 toward understanding temperature behavior for new design, filtering out temperature-related 157 effects (statically or dynamically), or tracking their response with time. To date, the use of TB 158 data within the St-Id or SHM paradigms has been limited [40-42]. This paper presents recent and 159 future work to bring temperature to the forefront of long-span bridge evaluation. 160

TEMPERATURE-BASED STRUCTURAL IDENTIFICATION (TBSI) 161 TBSI follows the conventional stages of the Structural Identification (St-Id) processes with 162 specific temperature-based techniques applied to Stages 3, 4 and 5 (see the outer circle of Figure 163 3). Typically Stage 3 of the St-Id process involves a static load test (for short- to medium-span 164 bridges) or various forms of dynamic testing for the experiment. TBSI uses a temperature-based 165 experiment in Stage 3 with the goal of measuring the input-output thermal behavior of the 166 structure. One of the primary reasons this experimental approach is utilized is due to the fact that 167 many bridge parameters (bearings, joints, connections, etc.) are highly sensitive to temperature 168 variations thus allowing for adequate identification. 169

170 Figure 3: Structural Identification Stages 171

A pilot study was conducted on a 168m (550ft) steel tied-arch span of the Tacony-172 Palmyra Bridge, designed and constructed in 1929 by Ralph Modjeski (Figure 4). The primary 173 findings are provided herein with the full details presented in Yarnold et al. [43]. The goal of the 174 study was to apply the TBSI methodology on an operating bridge. Observation and 175 conceptualization of the structure (Stage 1) was performed for the steel bridge which included 176

(1) Observation & Conceptualization

(2) A-Priori Modeling

(3) Controlled Experiment

(4) Processing & Interpretation of

Data

(5) Model Calibration

and Parameter Id

(6) Utilization of Model for

Simulations

St-Id


review of all existing plans and inspection reports. Then an a-priori finite element (FE) model 177 was developed using Strand7 software (Stage 2). This model was then used to conduct sensitivity 178 studies of different parameters as a result of temperature change. From this study an 179 instrumentation plan was developed to physically measure the input-output temperature 180 relationship along the steel tied-arch bridge. 181

182 Figure 4: Steel Tied-Arch Pilot Study Bridge 183

In December 2010 the temperature-based instrumentation was installed (Stage 3). This 184 included 56 vibrating wire strain gages (with thermistors), 2 vibrating wire displacement gages 185 (with thermistors), 2 data acquisition systems, and a weather station. Data was recorded for 186 several months and then it was processed and interpreted (Stage 4). 187

Model calibration (Stage 5) was performed by subjecting the numerical model to the 188 same measured temperature variations and attempting to match the strains and displacements. 189 This was conducted using 24 hour data sets with measurements every 3 minutes. Figure 5 190 graphically illustrates an example of the measured and numerical model results before and after 191 calibration. Through this process, a refined estimate of connection, bearing, and boundary 192 stiffnesses were found allowing for more accurate simulation capabilities of the model. For 193 example, the behavior of the bearings was better understood after the calibration process. It can 194 be seen from Figure 5 that the strain versus displacement behavior was initially modeled as 195 linear. However, the expansion bearing stiffnesses were refined to better represent the actual 196 nonlinear “stick-slip” type behavior. This information was helpful for assessment of the bearings 197 themselves as well as performing other scenario analyses. 198


199 Figure 5: Measured versus Model Results at the West End Middle Chord (Upstream) [43] 200

A second goal of the pilot study was to conduct a comparative assessment with an 201 independent St-Id of the same bridge using ambient vibration testing (AVSI). The ambient 202 vibration test was carried out using 48 uniaxial accelerometers positioned to identify the primary 203 modes in the vertical, transverse, and longitudinal directions. Full details of the experiment and 204 data processing can also be found in Yarnold et al. [43]. The processed results were utilized for 205 conventional finite element model calibration matching natural frequencies and MAC (modal 206 assurance criterion) values. The results indicate that TBSI and AVSI are synergistic providing 207 complementary information related to a diverse range of structural performances. In addition, the 208 results illustrated several TBSI strong-points over AVSI, including the following: 209

• enhanced ability to identify boundary conditions 210 • enhanced ability to identify continuity conditions 211 • capability to identify both linear and nonlinear behaviors 212 • drastically reduced data storage, data processing, and time synchronization requirements 213

TEMPERATURE-BASED STRUCTURAL HEALTH MONITORING (TBSHM) 214 The main approach for TBSHM is to leverage the input-output temperature relationship for 215 mapping a stable and unique signature of the structure. An effective SHM system must develop 216 and characterize a signature of the structure that is insensitive to normal operational changes. 217 Therefore, this signature can be used as a baseline to help detect variations of the structure 218 (potentially as a result of damage) which drives proactive maintenance and preservation. 219

After the pilot bridge was utilized for TBSI (described in the prior section) it was 220 continually monitored to evaluate the feasibility of TBSHM. Research to date has shown a 221 temperature-based signature to be a good selection for SHM applications. The pilot bridge 222 monitoring found the three-dimensional (3D) relationship between local, global, and temperature 223 measurements was a robust signature for the steel tied-arch bridge. Specifically, the local 224 measurements were mechanical strains of the members where the global measurements were 225 displacements at the end of the structure. The pilot bridge found that a unique data surface 226 developed at each location of the arch. These surfaces were studied as baselines for the SHM 227 system where the changes in bounds and/or orientation could be used as indicators of abnormal 228


behavior, potentially as a result of damage. Figure 6 illustrates the 3D surface concept. Full 229 details of the study are provided in Yarnold and Moon [44]. 230

231 Figure 6: Illustration of the 3D Surface Baseline 232

As part of the pilot project, a numerical simulation study was performed to compare a 233 conventional vibration-based SHM system and the new TBSHM system. The study was 234 conducted by utilizing the calibrated FE models from the steel tied-arch bridge. Then a series of 235 realistic damage scenarios (some of which had occurred on the structure in the past) were 236 selected and numerically simulated. This information was used for independent sensitivity 237 studies of a vibration-based SHM system baseline as well as a TBSHM system baseline. The 238 vibration-based baseline utilized the primary frequencies and mode shapes of the structure. The 239 temperature-based baseline used the strain-displacement-temperature relationship. The primary 240 findings were as follows: 241

• The vibration-based SHM baseline was relatively insensitive to local and global damage 242 scenarios. Changes to the natural frequencies and modes shapes were negligible (less 243 than 5% for nearly all scenarios). This indicated a vibration-based baseline to have 244 limited potential to identify structural changes. 245

• The TBSHM baseline was sensitive to localized effects, but this was dependent on the 246 resolution of the instrumentation. Sensors realistically need to be placed within one bay 247 of the damage to be reliably identified. 248

• The TBSHM baseline was highly sensitive to global changes of the structure (over a 249 hundred percent change for most scenarios). The 3D baseline is able to readily identify 250 damage scenarios that involve the primary structural members. 251

PERIODIC TEMPERATURE-BASED ASSESSMENT (PTBA) 252 PTBA is a bridge evaluation method which aims to supplement visual inspection through 253 measured input-output temperature responses without the use of a finite element model. The 254 method provides quantitative information for assessment of selected critical movement 255

Best Fit 3D Plane

Error Term


mechanisms (e.g. bearings, joints and slotted connections). This evaluation is performed through 256 design and execution of specific performance metrics. 257

PTBA was demonstrated on the steel tied-arch pilot bridge described above. The primary 258 objective of the study was to first assess the current performance of the bridge bearings (shown 259 in Figure 7) and resulting influence on the structural system. In addition, to identify their 260 remaining service life and/or recommend a time frame for the next in-depth evaluation. The 261 expansion bearings were identified as critical mechanisms since they allow for adequate thermal 262 movement of the structure reducing any build-up of thermal forces. 263

264 Figure 7: Expansion Bearing at the West End of the Arch (also shows VW displacement 265

gage location) 266

Performance metrics were then established for quantitative assessment of the bearings. 267 The first metric was to calculate the percentage of measured to theoretical movement. The goal 268 was to identify the actual movement occurring at each bearing. If the movement was more or less 269 than anticipated, then further evaluation would be justified to understand why. The second metric 270 was the calculation of the observed coefficient of friction at each bearing for comparison with 271 the manufacturer’s specified values. This was to ensure that excessive thermal force build-up 272 was not occurring. The last metric was to calculate the overall percentage of recovery of the 273 structure at specific time intervals (Figure 8). This was to ensure that long-term drift was not 274 present which could be an indicator of continual degradation occurring at a location along the 275 structure. 276

Expansion Bearing

VW Displacement Gage


277 Figure 8: Global Recovery Illustration [45] 278

The monitoring system currently in place along the arch was used to evaluate the three 279 metrics. Approximately one year of data was utilized. This data were processed and the 280 performance metrics were evaluated. The arch bearings were found to be performing adequately. 281 This conclusion was drawn from the following findings: (1) 97% to 98% of the theoretical 282 longitudinal movement was measured, (2) the bearing coefficient of friction was determined to 283 be 0.07 which was within the manufacturers specification (ranging from 0.045 to 0.10 based on 284 the temperature), and (3) 95% recovery of the structure was observed. Full details of the study 285 are presented in Yarnold and Dubbs [45]. The primary conclusions for future applications of the 286 PTBA method are as follows: 287

• PTBA can provide more reliable, quantitative, information with regard to the 288 performance of movement mechanisms (e.g. expansion bearings) compared to visual 289 inspection. 290

• Clear performance metrics must be established beforehand that quantitatively assess the 291 performance of the system which allow for an objective and quantifiable measure of 292 component performance. 293

• Monitoring system should (1) provide measurements at both the local and global levels, 294 (2) consist of redundant sensors, (3) characterize in-situ coefficient of thermal expansions 295 and incorporate it with instrumentation where applicable, (4) select the appropriate 296 sensing technology for the mechanism time scale, and (5) utilize reliable sensing 297 hardware. 298


CONCLUSIONS AND RECOMMENDATIONS 299 Temperature-based (TB) evaluation and monitoring techniques for long-span steel bridges were 300 presented. The underlying TB concept is based on the notion that temperature variations can be 301 treated as measureable loading for bridges and thus can be used to obtain a complete input-302 output relationship for structural evaluation. This is achieved through instrumentation of select 303 members and movement systems. Each location includes sensing for measurement of the 304 temperature (input) along with the corresponding strains, displacements, and/or rotations 305 (output). These measured relationships can identify a unique signature (or baseline) of the 306 structure. The TB signature can be utilized within the structural identification framework, termed 307 TBSI. This signature may also be leveraged for identification of unusual structural behaviors 308 within a structural health monitoring framework, termed TBSHM. In addition, the signature may 309 be utilized to supplement visual inspection, termed periodic temperature-based assessment 310 (PTBA). 311

The primary advantage common to all three TB evaluation techniques is that a TB 312 signature is highly sensitive to many structural changes that could be result of damage or 313 deterioration. This increases the potential for identification and/or monitoring such issues. Each 314 of the methods has shown excellent results for evaluation of movement mechanisms such as 315 expansion bearings, joints, connections, etc. This signature is also capable of identifying both 316 linear and nonlinear behaviors. In addition, a TB signature is logistically better than vibration-317 based signatures due to the high signal-to-noise of the measured responses, negligible data 318 storage requirements, inexpensive equipment, and minimal time synchronization requirements. 319

Several recommendations are provided below for bridge owners considering the use of 320 TBSI, TBSHM, or PTBA. 321

• TBSI is a comprehensive approach that is highly beneficial for obtaining a detailed 322 understanding of boundary and/or continuity conditions of a structure. This approach 323 requires reasonably dense instrumentation along with construction of a numerical model. 324

• TBSHM allows for a robust technique for long-term tracking of global structural 325 behavior with sparse instrumentation. For tracking localized effects a dense 326 instrumentation network should be utilized, but allows for increased anomaly detection. 327

• PTBA can be applied for critical movement system and add significantly more 328 quantitative information with regard to performance than visual assessment. Only 329 minimal instrumentation is required. 330

FUTURE RESEARCH 331 The temperature-based (TB) evaluation and monitoring techniques are still being researched due 332 to their potential impact toward identification and monitoring of bridges and other structures. 333 Currently, several short- to medium-span bridges are being studied for the use of these TB 334 methods on different span lengths and structure types. This includes two steel plate girder 335 bridges, a steel through truss bridge, and a cast-in-place prestressed-concrete pedestrian bridge. 336 The results of the pilot study described earlier in the paper along with the results from these 337 studies will be combined for refined frameworks of the different TB methodologies. In addition, 338 the results will be used to design and install the first true application of TBSI and TBSHM along 339 the Hurricane Bridge, which is a long-span cantilever truss bridge owned by the Tennessee 340 Department of Transportation (Figure 9). 341


342 Figure 9: Hurricane Bridge 343

ACKNOWLEDGMENTS 344 This material is based upon work supported by the National Science Foundation (NSF) under 345 Grants No. CMMI-1434373 and CMMI-1434455. Any opinions, findings, and conclusions or 346 recommendations expressed in this material are those of the authors and do not necessarily 347 reflect the views of the National Science Foundation. The authors would also like to express 348 gratitude to the Tennessee Department of Transportation for their support of this work. The pilot 349 study was performed with the support of NSF grant EEC-0855023 and precious help of The 350 Burlington County Bridge Commission, Intelligent Infrastructure Systems, and Pennoni 351 Associates. 352


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