Due to conservative approaches in construction design and practice, infrastructure often has hidden reserve capacity. When quantified, this reserve has potential to improve decisions related to asset management. Field measurements, collected through load testing, may help in the identification of unknown model-parameter values and this task is called structural identification. Then, the reserve capacity is assessed using the updated behaviour model. The quality of model updating depends on the choice of the measurement system, including sensor types and locations. In most practical applications, these sensor systems are designed using engineering judgement based on experience and signal-to-noise ratios. However, finding the optimal design is difficult due to redundancies in information gained from sensors. The information gain of each possible sensor location can be quantitatively evaluated using our hierarchical algorithm in order to select the best location. When multiple sensors are involved in the configuration, this algorithm explicitly accounts for the mutual information between sensors, thus avoiding redundancy in information gain. In this study, near-optimal measurement systems, given by the hierarchical algorithm, are compared with measurement systems that were configured by engineers in terms an information-gain metric: joint entropy. For this comparison, three full-scale case studies are used: The Singapore Flyover Bridge (Singapore), the Exeter Bascule Bridge (United Kingdom) and the Rockingham Bridge (Australia). For all case studies, measurement systems provided by the hierarchical algorithm have outperformed sensor configurations chosen by engineers. Using a quantitative methodology to design measurement systems thus leads to a potentially higher information gain compared with engineering judgement alone. This is expected to improve the quality structural identification and subsequent management decisions.