5.1 Timing Scale of BEAST Experiment

indexes are developed to define the bridge component performance based on the variety of multiple NDE techniques. These indexes are then compared against the representative performance of the bridge and thresholds for each NDE technique are derived accordingly. Ultimately, the NDE data is fused using the proposed fusion methodology and then compared against the performance of the bridge. Final recommendations is made at the conclusion of this section based on the results observed from the analysis of NDE data. 5.1 Timing Scale of BEAST Experiment In order to better understand the performance of bridge components over long time‐scales it is essential to find matrices that scale the timing at the BEAST facility with the timing in which a typical bridge deteriorates in the real world. To do so, multiple factors can be used as the scaling system to normalize the accelerated timing of BEAST versus real world timing. Among all factors, truck traffic and environmental characteristics are likely the most important and influential external parameters that shall be considered during this scaling. There are other factors, such as internal characteristics of bridge components (including the speed of chemical reaction required for the corrosion of rebars or the chloride ion penetration), that could be used as the scaling system. Due to complexity of such scaling and the extensive time/effort required, this project only concentrated on the scaling based on external parameters. At the same time, it is worth mentioning that external parameters, such as the live and environmental loading, are the most influential factors in determining bridge deterioration. Truck Traffic Live-Load – in comparison with regular car traffic, truck traffic induces the most damage to the concrete decks. Therefore, the average daily truck traffic (ADTT) is scaled-up in the BEAST experiment based on the same level of traffic that a bridge is subjected to in the real world. To that extent, the ADTT information associated with all the bridges in the state of Pennsylvania was derived from InfoBridge. Figure 36 demonstrates the frequency histogram of ADTT recorded in 2020 for the bridges in the State of Pennsylvania. Given the wide distribution of recorded ADTT, the 50% percentile (median) of the recorded ADTT (equals to 137) is taken as the representative level of truck traffic that a regular highway bridge might be subjected to during most days of the year. By reviewing Table 2, the BEAST facility has already subjected to over 2 million cycles of live-load traffic. Therefore, it is possible to establish a correlation between the BEAST timing versus the real-world timing based on the live-load. Table 6 proposes such scaling based on the comparison of live-load demand on BEAST as well as a representative bridge with 137 trucks per day (ADTT). Figure 37 plots the conversion of BEAST live-load cycles into actual years that a bridge with ADTT equals to 137 would be typically exposed.

Figure 36. Frequency histogram of ADTT for bridges in Pennsylvania Table 6. Correlation between applied traffic cycles on BEAST and actual years BEAST Traffic Cycle* Actual Year ** 185000 3.7*** 385000 7.7 572000 11.4 717000 14.3 914000 18.3 1114000 22.3 1323270 26.5 1374876 27.5 1671506 33.4 1866006 37.3 2000000 40.0 * Reflects the timing in which BEAST was stopped and NDE data was collected ** According to the number of years calculated based on ADTT=137 *** Example: 185000/(365*137)=3.7 Yrs

Figure 37. Conversion of BEAST live-load cycles into actual years

Environmental Loading – Similar to the live load, the levels of freeze-thaw cycles were derived from the InfoBridge for the bridges in Pennsylvania. By reviewing Table 2 and comparing to the annual freeze-thaw cycles shown in Figure 38, the limited number of freeze-thaw cycles that the BEAST was exposed to (85 cycles) did not correlate with real world conditions.

Figure 38. Frequency histogram of Annual Freeze-thaw Cycles for bridges in Pennsylvania