Editorial for Special Issue

A “structure” is here understood to include anything constructed by man—underground space, buildings, transmission towers, pipelines, distribution grids, economic systems, air frames, measuring, data acquisition and manipulation systems, etc. The design and control of these infrastructure elements can all fit within the purview of Civil Engineering. They all affect the public well-being; they all involve aspects of what traditionalists define as civil engineering. Some elements demand expansion of expectations and imagination, but provide the profession with new opportunities in growing application spaces, provide powerful tools, more interesting and cutting edge problems to solve. This growth could also attract the attention of many students who currently choose other fields of engineering, science, public policy, business, and economics, and minimize reliance on other disciplines. The civil engineering profession has much to offer society, and everyone would benefit by our addressing more of the world.

An example of the empowerment of civil engineers through integration of new technologies is enlightening. Integrated health monitoring of structures, made possible by advances in sensor technology, is improving structural reliability, longevity, system performance, and safety against natural hazards and intentional attack. Current technology is moving beyond the embryonic stage and is becoming able to meet the challenges associated with structural health interpretation. Perhaps the most important enabling technology for structural health monitoring has been the introduction of comprehensive miniature sensing platforms that colocate transduction (sensing or actuation), signal processing, computational power, and wireless communication all in one miniaturized package. Such wireless sensors are commonly called motes, or “smart” sensors.

In turn, motes are combined into large, organic networks that allow dense, detailed sensing, thereby opening a new sensing paradigm in which the network is effectively the sensor. The paradigm of sensor networking allows engineers and scientists to move beyond the idea of a sensor as a single instrument that only measures one thing, to a comprehensive system consisting of many small nodes working cooperatively. Motes are now ubiquitous enough that several companies, large and small, are selling them commercially.

Today, civil engineers are in a good position to refine existing measurement archetypes by utilizing emerging sensor design archetypes; in particular, transducers and micro-circuitry made possible by micro electro-mechanical systems (MEMS) technology. MEMS are micron-scale mechanical devices that are “machined” out of silicon using the same processes commonly used to fabricate integrated circuits. These are often the same mechanical subassemblies that are part of their traditional macro-scale counterpart. Another attractive feature of MEMS is the ability to seamlessly integrate electrical and electro-mechanical components alongside the micro-machined mechanical transducer. Because the process elements and internal linkage movements are now small, MEMS-based transducers often consume very little power. The low-cost, low-power, and small-size attributes of MEMS-based transducers have revolutionized what can now be measured in situ.

For systems typically covering relatively large areas, data from spatially distributed sensor networks becomes necessary to reliably determine operating environments, and to detect damage during or after extreme events. The scalability and expandability of sensor networks are topics of particular importance as both attributes enable owners to gradually add more sensing channels into their monitoring systems, as future budgets permit. Monitoring large-scale civil engineering structures such as long span bridges requires a large number of sensors of different types. Even higher data rates could be produced using high-resolution sensors, such as 24-bit sensors. For instance, for the Golden Gate Bridge experiment (Paksad et al. 2008, this issue), it took about nine hours to download the network when all the nodes had full buffers (i.e., about 30 megabytes of data). This is not a viable paradigm. Innovative transducer data compression techniques are thus extremely important for data transmission, storage, and retrieval of large volumes of sensor data generated from large scale sensor networks. This is especially true after a strong seismic event, when communication bandwidth may be scarce due to damage of communication networks and rise in emergency communication use. Furthermore, data compression provides some level of encryption to the sensor data, and thus prevents unauthorized outsiders from sniffing or even modifying it over the data transmission path.

Perhaps the major challenge for health monitoring of civil engineering structures lies in developing robust damage (or condition) models. In typical mechanical and aerospace structures, the damage is generally well defined (locations and symptoms) and understood (e.g., cars, machines, airplanes, etc.). This allows sensors (sensor networks, algorithms, etc.) to be optimally developed for measuring and monitoring structural conditions and detecting damage. Conversely, for most civil engineering structures under hazard or normal (aging) conditions, there are no “equivalent” damage models. It is difficult to identify needs in sensor technology research without knowing the location, the measurement conditions, and the kind of infrastructure we must contend with. After we know how to properly define archetypical damage states of civil structures, we will be in a stronger position to address what to measure and how to directly interpret or relate measured signals to the condition of the instrumented structure.

To date, sensing in large scale infrastructure systems involves transducers placed in a known location on a static structure. The next step is to integrate mobility, a new key component to automation and monitoring, into the realm of possibility. This is becoming possible due to advances in the miniaturization of sensors, the progress in robotics platforms, and the convergence of communication and multi-media platforms. While information moves through communication channels in, to borrow from a colleague, “cyberphysical” networks, the sensing mechanisms themselves have recently acquired mobility, which integrates itself as a dynamical component of an infrastructure.

The control and optimization of cyberphysical infrastructure systems presupposes the knowledge of constitutive principles of such (“physical”) systems, and a good understanding of (“cyber”) information flow underlying their behavior. A possible meaning of cyber is thus “virtual” or “computer related,” while physical relates to system features governed by a physical process. A possible definition for cyberphysical network is thus a network that is governed at the same time by constitutive equations (conservation laws, laws of physics, etc.), and information theory (communication, sensing). Examples of such mobile infrastructure systems include:It is a brave new world.