Subsurface Characterization at Ground Failure Sites in Adapazari, Turkey

Ground failure in Adapazari, Turkey during the 1999 Kocaeli earthquake was severe. Hundreds of structures settled, slid, tilted, and collapsed due in part to liquefaction and ground softening. Ground failure was more severe adjacent to and under buildings. The soils that led to severe building damage were generally low plasticity silts. In this paper, the results of a comprehensive investigation of the soils of Adapazari, which included cone penetration test (CPT) profiles followed by borings with standard penetration tests (SPTs) and soil index tests, are presented. The effects of subsurface conditions on the occurrence of ground failure and its resulting effect on building performance are explored through representative case histories. CPT- and SPT-based liquefaction triggering procedures adequately identified soils that liquefied if the clay-size criterion of the Chinese criteria was disregarded. The CPT was able to identify thin seams of loose liquefiable silt, and the SPT (with retrieved samples) allowed for reliable evaluation of the liquefaction susceptibility of fine-grained soils. A well-documented database of in situ and index testing is now available for incorporating in future CPT- and SPT-based liquefaction triggering correlations.     

Design and Comparison Base Analysis of Adaptive Estimator for Completely Unknown Linear Systems in the Presence of OE Noise and Constant Input Time Delay

In this paper, an adaptive estimator (AE) is introduced to learn the approximate Q‐function and control policy by only using the noisy states and control signals of the unknown linear, discrete time systems having constant input time delay. The system measurements are uncertain owing to output error (OE)‐type noise acting randomly on the system measurements. Therefore, this research differs from the designed AE in the literature since previous research ignores the role of the external random disturbances on AE‐based learning. In order to compare the AE‐based learning results with traditional system identification (SI) approaches, a modified version of the OE model structure for unstable systems is reviewed and parameters of a second‐order unstable system with constant input time delay are identified. The simulation results demonstrate that the designed AE efficiently minimizes the stochastic cost function and the temporal difference error by learning the approximate solution for the Hamilton‐Jacobi‐Bellman (HJB) equation. It is noted that the error in the Q‐function obtained with the AE is slightly larger than the Q‐function attained with the identified OE parameters. However, as the noise standard deviation increases, the error in the AE‐based learning results reduces whereas the error in the OE‐based learning increases. This indicates that even though the added random noise deteriorates the performance of the OE predictor, it improves the learning efficiency of the AE since it acts like exploration noise.     

Improved assignment with ant colony optimization for multi-target tracking

Detecting and tracking ground targets is crucial in military intelligence in battlefield surveillance. Once targets have been detected, the system used can proceed to track them where tracking can be done using Ground Moving Target Indicator (GMTI) type indicators that can observe objects moving in the area of interest. However, when targets move close to each other in formation as a convoy, then the problem of assigning measurements to targets has to be addressed first, as it is an important step in target tracking. With the increasing computational power, it became possible to use more complex association logic in tracking algorithms. Although its optimal solution can be proved to be an NP hard problem, the multidimensional assignment enjoyed a renewed interest mostly due to Lagrangian relaxation approaches to its solution. Recently, it has been reported that randomized heuristic approaches surpassed the performance of Lagrangian relaxation algorithm especially in dense problems. In this paper, impelled from the success of randomized heuristic methods, we investigate a different stochastic approach, namely, the biologically inspired ant colony optimization to solve the NP hard multidimensional assignment problem for tracking multiple ground targets.     

Control-oriented modeling of a three-way catalytic converter with observation of the relative oxygen level profile

The paper presents a novel control-oriented, first-principle model of a three-way catalytic converter (TWC). The model accurately predicts the air–fuel ratio downstream of the catalyst and provides insights into the relative oxygen level (ROL) profile along the catalyst. The reaction mechanism of the TWC model is simplified to only two reactions, with oxygen being the only species stored on the active surface of the catalyst. All the information about the gas composition is taken from the upstream wide-range oxygen sensor. Additional model inputs are the exhaust gas temperature and the exhaust mass flow. The model is parameterized with only a few measurements from an engine test bench. Exact values of gas concentrations are not needed at any point.The ROL profile is a good indicator for the condition of the TWC. It can therefore be used to derive optimal TWC depletion strategies after fuel cut-off phases. Since the model is real-time capable, the ROL profile can even be used for on-line control strategies. Based thereon, a causal strategy is proposed with results that match those of the non-causal strategies found in off-line optimizations.     

An experimental study of the exergetic performance of an underground air tunnel system for greenhouse cooling

The present study highlights the exergetic performance characteristics of an underground air tunnel for greenhouse cooling with a 47 m horizontal, 56 cm nominal diameter U-bend buried galvanized ground heat exchanger. This system was designed and installed in the Solar Energy Institute, Ege University, Izmir, Turkey. Underground air tunnel systems, also known as earth-to-air heat exchangers, are recognized to be outstanding heating, cooling and air heating systems. On the other hand, they have not been used yet in the Turkish market. Greenhouses also have important economical potential in Turkey’s agricultural sector. Greenhouses should be cooled during the summer or hot days. In order to establish optimum growth conditions in greenhouses, renewable energy sources should be utilized as much as possible. It is expected that effective use of underground air tunnels with a suitable technology in the modern greenhouses will play a leading role in Turkey in the foreseeable future. The exergy transports between the components and the destructions in each of the components of the system are determined for the average measured parameters obtained from the experimental results. Exergetic efficiencies of the system components are determined in an attempt to assess their individual performances and the potential for improvements is also presented. The daily maximum cooling coefficient of performances (COP) values for the system are also obtained to be 15.8. The total average COP in the experimental period is found to be 10.09. The system COP was calculated based on the amount of cooling produced by the air tunnel and the amount of power required to move the air through the tunnel, while the exergetic efficiency of the air tunnel is found to be in a range among 57.8–63.2%. The overall exergy efficiency value for the system on a product/fuel basis is found to be 60.7%.     

Derivation of a parameter stabilizing training criterion for adaptive neuro-fuzzy inference systems in motion control

This paper presents a novel training algorithm for adaptive neuro-fuzzy inference systems. The algorithm combines the error back-propagation algorithm with the variable structure systems approach. Expressing the parameter update rule as a dynamic system in continuous time and applying sliding mode control (SMC) methodology to the dynamic model of the gradient based training procedure results in the parameter stabilizing part of training algorithm. The proposed combination therefore exhibits a degree of robustness to the unmodelled multivariable internal dynamics of the gradient-based training algorithm. With conventional training procedures, the excitation of this dynamics during a training cycle can lead to instability, which may be difficult to alleviate owing to the multidimensionality of the solution space and the ambiguities concerning the environmental conditions. This paper shows that a neuro-fuzzy model can be trained such that the adjustable parameter values are forced to settle down (parameter stabilization) while minimizing an appropriate cost function (cost optimization), which is based on state tracking performance. In the application example, trajectory control of a two degrees of freedom direct drive robotic manipulator is considered. As the controller, an adaptive neuro-fuzzy inference mechanism is used and, in the parameter tuning, the proposed algorithm is utilized.     

Experimental investigation of the performance of a solar‐assisted ground‐source heat pump system for greenhouse heating

The main objective of the present study is to investigate the performance characteristics of a solar‐assisted ground‐source heat pump system (SAGSHPS) for greenhouse heating with a 50 m vertical 1¼ in nominal diameter U‐bend ground heat exchanger. This system was designed and installed in the Solar Energy Institute, Ege University, Izmir (568 degree days cooling, base: 22°C, 1226 degree days heating, base: 18°C), Turkey. Based upon the measurements made in the heating mode, the heat extraction rate from the soil is found to be, on average, 54.08 Wm^?1 of bore depth, while the required borehole length in meter per kW of heating capacity is obtained as 12.57. The entering water temperature to the unit ranges from 8.2 to 16.2°C, with an average value of 9.1°C. The greenhouse air is at a maximum day temperature of 25°C and night temperature of 14°C with a relative humidity of 40%. The heating coefficient of performance of the heat pump (COP_HP) is about 2.13 at the end of a cloudy day, while it is about 2.84 at the end of sunny day and fluctuates between these values in other times. The COP values for the whole system are also obtained to be 5–15% lower than COP_HP. Copyright © 2004 John Wiley & Sons, Ltd.     

A highly selective and sensitive benzothiazole‐based ‘turn‐on’ fluorescent sensor for Hg2+ ion

A simple benzothiazole‐based fluorescent probe (TDA) for the determination of Hg²⁺ ion in aqueous solutions was synthesised in one step and characterised by ¹H NMR, ¹³C NMR, APT, COSY, FTIR, and elemental analysis. TDA shows a significant fluorescence change upon the interaction of Hg²⁺ ion in DMF–water (v/v = 1/1), while only minor changes in fluorescence intensity are observed with 18 other metal ions. Fluorescence enhancement by a factor of 15 is achieved upon selective interaction with Hg²⁺ ion. The Hg²⁺ ion detection process is found to be pH dependent; therefore, TDA could be feasible within a pH range of 4.0–7.0.