Information technologies are increasingly facilitating life-long monitoring for civil infrastructure applications. Sensor data, including video signals, can be used for long-term structural condition assessment, traffic-load regulation, and emergency response following earthquakes or other natural / man-made hazards. In such a strategy, data from thousands of sensors may be analyzed with real-time and long-term assessment and decision- making implications. Addressing the above, a flexible and scalable framework has been developed and implemented. This framework networks and integrates on-line real-time heterogeneous sensor data, computer vision, and archiving systems. Two integrated systems for structural health monitoring have been established as demonstration testbeds located on the University of California, San Diego (UCSD) campus. These systems handle all tasks of monitoring including, data acquisition, data transmission, data archiving, and database querying. From a bridge-deck testbed, the use of time synchronized sensor plus video data has been pioneered. Using this testbed, over 400,000 sets of synchronized traffic-induced strain time histories with video have been recorded and archived over a three year period. Using extracted features from image processing, unique cleansed datasets of labeled traffic, well suited for use with supervised machine learning algorithms, have been created and are available on-line. Elements of this dataset are employed for estimation of traffic speed and vehicle classification efforts (in collaboration with co-workers). A computational model of the bridge deck system was created to provide strain time histories similar to those actually recorded. Within the numerical simulation framework, it is shown that neural networks perform satisfactorily in providing accurate estimates of vehicle speeds, wheelbases, and axle weights. A neural network-based damage detection methodology has also been constructed, verified on the numerically simulated data, and tested on the recorded data. An integrated system for structural health monitoring, ready for deployment at the thousands of heterogeneous sensors level, has been deployed on the Voigt Drive / Interstate-5 overcrossing testbed, also on campus at UCSD. An important aspect of this 4-span, 2-lane bridge testbed lies in its availability for collaborative interdisciplinary research. System details and data from a shakedown test conducted with a pilot sensor array are presented and discussed

Despite the recent prevalence of severe drought, California faces a broadly underappreciated risk of severe floods. Here, we investigate the physical characteristics of "plausible worst case scenario" extreme storm sequences capable of giving rise to "megaflood" conditions using a combination of climate model data and high-resolution weather modeling. Using the data from the Community Earth System Model Large Ensemble, we find that climate change has already doubled the likelihood of an event capable of producing catastrophic flooding, but larger future increases are likely due to continued warming. We further find that runoff in the future extreme storm scenario is 200 to 400% greater than historical values in the Sierra Nevada because of increased precipitation rates and decreased snow fraction. These findings have direct implications for flood and emergency management, as well as broader implications for hazard mitigation and climate adaptation activities.

Trimethylsilylalkyne derivatives are transformed into cyclic β-silylalkenyl triflates through cationic cyclization and subsequent trapping of the alkenyl cation by a triflate anion. β-Silylcyclohexenyl triflates and 3-trimethylsilyl-1,4-dihydronaphth-2-yl triflates are generated efficiently using this methodology. These products provide ready access to substituted cyclohexynes, exemplified by a concise total synthesis of β-apopicropodophyllin.