Nowadays, with the aging of the bridges and the advancements in technology, load testing has emerged as an effective method to assess existing concrete bridges with missing information, or where analytical methods do not provide an accurate assessment. Two types of load tests are identified: diagnostic load tests and proof load tests. Both rely on field measurements of parameters or structural responses of the bridge during the test. A diagnostic load test measures the response of the bridge so that analytical models can be calibrated and evaluated. In a proof load test, the bridge directly demonstrates that it can carry a certain load. Since large loads are applied, the bridge needs to be carefully monitored. In this case, monitoring the measurements provide a warning to avoid damage. This paper reviews the literature on reported load tests and the measurement techniques used during these tests. It also includes a review of traditional and recently developed sensing technologies. Finally, the measurement requirements for diagnostic and proof load tests are given as well as a flow chart to guide engineers in the selection process of appropriate monitoring and measurement techniques during load tests. This paper can serve engineers during the preparation of a load test.

In the Netherlands, many prestressed concrete bridge girders are found to have insufficient shear–tension capacity. We tested four girders taken from a demolished bridge and instrumented these with traditional displacement sensors and acoustic emission (AE) sensors, and used cameras for digital image correlation (DIC). The results show that AE can detect cracking before the traditional displacement sensors, and DIC can identify the cracks with detailed crack kinematics. Both AE and DIC methods provide additional information for the structural analysis, as compared to the conventional measurements: more accurate cracking load, the contribution of aggregate interlock, and the angle of the compression field. These results suggest that both AE and DIC are suitable options that warrant further research on their use in lab tests and field testing of prestressed bridges.

Advanced monitoring methods are required to identify stop criteria in proof-load tests. In this study, the combined methodology of two-dimensional digital image correlation and acoustic emission is investigated for its applicability for future implementation in field tests. The two monitoring systems are deemed to provide valuable insight with external measurements from digital image correlation and internal measurements from acoustic emission. Two overturned T-section reinforced concrete slabs (0.37 × 1.7 × 8.4 m) tested under laboratory conditions are used for the assessment. The first slab test served as a preliminary test to enable sensor placement and creation of a relevant loading protocol. The main scientific results lead to a proposal for a test procedure using the combined methodology based on results, observations, and experiences from an individual stop criteria assessment for the two methods. The results include full-field plots, an investigation of the time of crack detection and monitoring of crack widths with digital image correlation, and a qualitative assessment of activity vs. load followed by a quantitative evaluation of calm ratios using acoustic emission. The individual results show that both digital image correlation and acoustic emission can identify damage occurrence earlier than other secondary methods. At crack detection (415 kN), crack widths were measured at widths between 0.078 mm to 0.125 mm and can be monitored until reaching the stop criterion at 463 kN (Eurocode SLS threshold of w_max = 0.2 mm). The acoustic emission results were limited by the pre-defined loading protocol and thus, only indicated that damage occurred sometime between 300 kN and 500 kN (pre-defined load levels). Therefore, the proposal for test procedure involves a methodology, where the loading protocol may be updated during testing based on monitoring results and thus provide even more valuable data.