Many bridges in the country have reached their intended service life limit [1, 2]. Some of them do not pass current load-ratings or show deterioration such as corrosion and cracking. Monies for replacement and repair of bridges, however, are scarce. In order to keep these critical infrastructure components in operation, inspection, maintenance, and monitoring play a vital role. Existing monitoring approaches use sensors such as strain gages or accelerometers that capture a physical measurement at a point. One pressing problem is fatigue cracking in fracture critical bridge members, which can have disastrous consequences to the infrastructure and public safety [3-5]. Because detection of fatigue cracks can be difficult, it is essential that a sensing technology is utilized that is able to measure strains at a large number of points with high accuracy. One challenge by deploying a traditional array of strain gages or strain rosettes is the complexity in the wiring. Also, for reinforced or prestressed concrete structures, damage that may lead to catastrophic failure is typically associated with internal processes such as wire fracture that may not necessarily be detectable on the surface [6, 7]. Acoustic Emission (AE) monitoring techniques represent a possible solution to this problem [8, 9]. Often, however, it is not feasible to install a network of AE sensors due to the prohibitive costs associated with such a system. Current available technologies give bridge managers access to sparsely spaced sensors. These, unfortunately, do not allow reliable early detection of anomalies such as strain concentrations or cracks at locations of even modest distances away from the sensor. To infer localized anomalies, such forms of indirect sensing rely on complex algorithms whose reliability is challenged by practical noise sources (i.e., temperature, precipitation, and normal loading variability). Thus, a need exists for a cost-effective sensing approach that is able to incorporate a variety of sensors applied in form of very dense arrays to maximize the chances for capturing damage externally as well as internally at an early stage. The measurements should support the bridge owners for informed decision making. Our research addresses the need for direct sensing, where anomalies are sensed at close proximity via a dense array of sensors.