This paper presents results of a theoretical investigation of suction caissons installed in seabed deposits composed of dense sand and subjected to inclined uplift loading under fully drained conditions. Consideration is given to the effects of caisson embedment, load application point, load inclination angle, friction angle of the soil, dilatancy and the initial stress state, as defined by the value of K0. Simplified expressions for the inclined uplift capacity of suction caissons are developed, based on the results of the finite element study. Introduction Suction caissons have been employed with success as foundations for large gravity platforms and as anchors for floating structures in areas such as the North Sea, the Gulf of Mexico and the North-West Shelf of Western Australia. Previous studies of the uplift capacity of a suction caisson have mainly concentrated on cases where the caissons are embedded in cohesive soils, i.e., where the undrained condition is of prime importance. Erbrich4 investigated the vertical load carrying capacity of a suction caisson in a dense silica sand profile with clay layers using the commercial finite element package ABAQUS. His work appears to be the first published numerical investigation of the uplift capacity of a suction caisson embedded in sand. Soon after, Bye et al1 described the design analyses of the Europipe 16/11E and Sleipner T foundations, which were also embedded in dense sand. Analyses of the uplift capacity of the caissons were performed using a slip surface-type model, and an attempt was made to incorporate the effects of sand dilatancy and the accumulation and dissipation of pore water pressure. Static pullout capacities measured in field tests were also reported and compared to predictions based on classical bearing capacity theory. It is evident from this study that sand dilatancy, as well as the drainage characteristics of the soil, together with the duration of the loading, are dominant factors in determining the uplift capacity. The increased use of suction caissons in sandy soils defines a pressing need to develop reliable methods for predicting their capacity, particularly when they are subjected to inclined uplift loading. In the study described in this paper, a theoretical investigation has been carried out of suction caissons subjected to inclined uplift loading for cases where the seabed soil is dense sand. It is assumed that the sand behaves in a fully drained manner under the application of the caisson loads. The results should therefore be applicable to cases of sustained loading of caissons in sand. The typical problem considered in the study is illustrated in Fig. 1. The suction caisson is assumed to be cylindrical with an overall diameter d and an embedded length L. It is assumed that an inclined uplift force is applied at the caisson wall by a chain attached to a lug located at a depth D, and the force is applied at an angle ?a to the horizontal plane. In practice, it is usual for the loading to be applied through a chain fixed to the side of the caisson.
Effective fault detection and isolation can improve the safety, reliability and efficiency of the district heating system. In order to detect and locate the sensor, actuator and component faults in the district heating system with faster response speed and higher accuracy, a two-level fault detection and isolation scheme, consisting of upper-level classifier for system faults and lower-level classifier for subfaults, is developed based on convolutional neural networks. In consideration of the difficulty of obtaining the operation data of a real district heating system under various faulty states, a test benchmark model of an integrated energy based district heating system is built from the system level, which contains the renewable energy based water boilers, transmission networks, and heat load substations to examine the effectiveness of the proposed scheme. To improve the model reality, the dynamics of sensors and actuators, models of heat exchangers, and thermal inertia are considered in the district heating system, and Gaussian noises are added in the raw data signals. Nine kinds of system faults including sensor, actuator, component faults, along with three sub-fault types of bias, drift, complete failure, are investigated in the benchmark system. The performance of the proposed two-level fault detection and isolation scheme is evaluated under different data windows and Gaussian noises in the district heating system, and is compared with other data-driven methods including k-nearest neighbor, random forest and back propagation neural networks. Experimental results show that the two-level fault detection and isolation scheme can detect the faults in the district heating system accurately and robustly, and the proposed scheme has the potential to become an effective solution to real-time monitoring of faults in the district heating system. INDEX TERMS District heating system, fault detection and isolation, thermal inertia, data-driven, convolutional neural networks.
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