Highlights of the California GeoExchange Project
A two year project sponsored by the Pacific Gas & Electric Company, the California Energy Commission, and the National Geothermal Heat Pump Consortium was initiated in 1997 to increase public awareness, identify California-specific cost-effective technologies, and reduce market barriers for ground-coupled heat pumps. Major activities included demonstration projects, research and development of system and loop designs, and market evaluations. Four commercial projects, and seven single and multifamily residential projects consisting of 326 units, were initiated. Five of these sites were monitored for up to 16 months. Monitoring data were used to calibrate DOE-2 models, which were in turn used to develop performance data for technical evaluations and market studies. Technical evaluations were completed on sizing methods, flow optimization, loop design options, water heating options, hybrid systems, and loop irrigation. Vertical helix loop designs were developed, installed and tested. The project developed 600 pages of reports, including a business plan for GeoExchange in California, and a GeoExchange Handbook. Project recommendations were to distribute key project findings, continue R&D on near term technologies, continue support for R&D to reduce loop costs, and to expand DOE-2 modeling capabilities to accommodate a larger number of system design options.
Monitored ground source heat pump performance in northern California
Fourteen residential ground source heat pump installations in the greater Northern California area were monitored during the period of December 1996 to January 1998. Equipment power, capacity, and efficiency, and loop water temperatures and flow were recorded on a 15 minute basis. Monitoring results were used to calibrate the DOE2.1E hourly building simulation program ground loop model and DOE2.1E was then used to estimate energy savings and life cycle economics for various load, system, and installation configurations.
Application of neural networks in bridge health prediction based on acceleration and displacement data domain
The health condition of the bridge can be predicted through sensors’ reading in bridge monitoring. The 
sensors measure the acceleration and displacement of bridge response. The data is sent to the local server through the data acquisition. Interpretation of the data applied neural network in the localized server system. This paper aims to define performance of the acceleration and displacement data domain as input in applied neural networks. The architecture of neural networks’ model used an input layer, one and two hidden layers with n neurons and an output layer. The input layer consists of time-acceleration domain and time-displacement domain of the 
bridge due to earthquake loads. Meanwhile, the output layer consists of bridge condition level which is determined using finite-element analysis software. The training activation used Gradient Descent Back-propagation and activation transfer function used Log Sigmoid function. The bridge condition is categorized in a range 0 to 3, which indicates the extent of bridge health condition ranging from safe to high-risk level. 
The case study is 3 spans of box-girder’s bridge subject to four earthquakes loads. The results showed that the prediction of bridge health condition based on displacement data domain with one hidden layer is more acceptable compared with based on acceleration. The comparison obtains the recommendation of the best of data reading from the sensors to predict the bridge health condition. The application neural networks in the bridge 
health prediction can help the authorities to know the condition of the bridge due to earthquake at monitoring time, as the repair and maintenance of bridges can be performed as early as possible before the bridge was damaged.