Artificial immune systems based committee machine for classification application

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.A new adaptive learning Artificial Immune System (AIS) based committee machine is developed in this thesis. The new proposed approach efficiently tackles the general problem of clustering high-dimensional data. In addition, it helps on deriving useful decision and results related to other application domains such classification and prediction. Artificial Immune System (AIS) is a branch of computational intelligence field inspired by the biological immune system, and has gained increasing interest among researchers in the development of immune-based models and techniques to solve diverse complex computational or engineering problems. This work presents some applications of AIS techniques to health problems, and a thorough survey of existing AIS models and algorithms. The main focus of this research is devoted to building an ensemble model integrating different AIS techniques (i.e. Artificial Immune Networks, Clonal Selection, and Negative Selection) for classification applications to achieve better classification results. A new AIS-based ensemble architecture with adaptive learning features is proposed by integrating different learning and adaptation techniques to overcome individual limitations and to achieve synergetic effects through the combination of these techniques. Various techniques related to the design and enhancements of the new adaptive learning architecture are studied, including a neuro-fuzzy based detector and an optimizer using particle swarm optimization method to achieve enhanced classification performance. An evaluation study was conducted to show the performance of the new proposed adaptive learning ensemble and to compare it to alternative combining techniques. Several experiments are presented using different medical datasets for the classification problem and findings and outcomes are discussed. The new adaptive learning architecture improves the accuracy of the ensemble. Moreover, there is an improvement over the existing aggregation techniques. The outcomes, assumptions and limitations of the proposed methods with its implications for further research in this area draw this research to its conclusion

GPS Measured Response of a Tall Building due to a Distant Mw 7.3 Earthquake

The response of a 413‐m‐tall building to the 12 November 2017 M[subscript w] 7.3 earthquake 642 km from the building is measured with a Global Positioning System (GPS) receiver located near the top of the building and operating with a 1 Hz sampling rate. Nearby GPS and seismic stations measure the ground motion near the building. The ground motions have amplitudes of ∼40  mm⁠, while the top of the building moves by up to 160 mm. The building motion continues with levels greater than the noise level of the GPS measurement for about 15 min after the earthquake. After the ground‐motion excitation ends, the building motion decays with a time constant of ∼2  min and the beat between the two lowest frequency modes of deformation of the building can be seen. There are two large amplitude peaks in the building motion with magnitudes of 120 and 160 mm. The timing of the peaks is consistent with ground excitation in an 8.3–6.5‐s‐period (120–180 mHz) band, which covers the 7.25 and 5.81 s periods (138 and 172 mHz frequencies) of the fundamental modes of the building. The ground motions in this band show two large pulses of the excitation, which have timing consistent with the large amplitude building signals. The response of the top of the building is amplified by an order magnitude over the ground motions in this band. There is no apparent permanent displacement of the top of the tower.National Aeronautics and Space Administration (Grant NNX09AK68G

Ground Motion in Kuwait from Regional and Local Earthquakes: Potential Effects on Tall Buildings

Abstract In recent years, the construction of tall buildings has been increasing in many countries, including Kuwait and other Gulf states. These tall buildings are especially sensitive to ground shaking due to long period seismic surface waves. Although Kuwait is relatively aseismic, it has been affected by large (Mw > 6) regional earthquakes in the Zagros Fold-Thrust Belt (ZFTB). Accurate ground motion prediction for large earthquakes is important to assess the seismic hazard to tall buildings. In this study, we first analyze the observed ground motions due to two earthquakes widely felt in Kuwait: the 08/18/2014 Mw 6.2 earthquake, 360 km NNE of Kuwait City, and the 11/12/2017 Mw 7.3 earthquake, 642 km NNE of Kuwait City. The peak spectral displacement periods of the ground motion from the 08/18/2014 Mw 6.2 earthquake matched well with the ambient vibration spectra of the tallest building—the Al-Hamra Tower. We calculate the ground motions from potential regional and local earthquakes. We use a velocity model obtained by matching the observed seismograms of the 2014 and 2017 earthquakes. We calculate ground motions in Kuwait due to a regional Mw = 7.5 earthquake, and a local Mw = 5.0 earthquakes. Our study shows that a significant source of seismic hazard to tall buildings in Kuwait comes from the regional tectonic earthquakes. However, local earthquakes have the potential to generate high peak ground accelerations (~ 98 cm/s2) close to their epicenters