A critical study of major construction projects in kerala vis-a-vis effectiveness in project implementation

Chapter 1 presents a brief note on the state at which the construction industry stands at present, bringing into focus the significance of the critical study. Relevance of the study, area of investigation and objectives of the study are outlined in this chapter. The 2nd chapter presents a review of the literature on the relevant areas. In the third chapter an analysis on time and cost overrun in construction highlighting the major factors responsible for it has been done. A couple of case studies to estimate loss to the nation on account of delay in construction have been presented in the chapter. The need for an appropriate estimate and a competent contractor has been emphasised for improving effectiveness in the project implementation. Certain useful equations and thoughts have been formulated on this area in this chapter that can be followed in State PWD and other Govt. organisations. Case studies on project implementation of major projects undertaken by Government sponsored/supported organizations in Kerala have been dealt with in Chapter 4. A detailed description of the project of Kerala Legislature Complex with a critical analysis has been given in this chapter. A detailed account of the investigations carried out on the construction of International Stadium, a sports project of Greater Cochin Development Authority is included here. The project details of Cochin International Airport at Nedumbassery, its promoters and contractors are also discussed in Chapter 4. Various aspects of implementation which led the above projects successful have been discussed in chapter 5. The data collected were analysed through discussion and perceptions to arrive at certain conclusions. The emergence of front-loaded contract and its impact on economics of the project execution are dealt with in this chapter. Analysis of delays in respect of the various project narrated in chapter 3 has been done here. The root causes of the project time and overrun and its remedial measures are also enlisted in this chapter. Study of cost and time overrun of any construction project IS a part of construction management. Under the present environment of heavy investment on construction activities in India, the consequences of mismanagement many a time lead to excessive expenditure which are not be avoidable. Cost consciousness, therefore has to be keener than ever before. Optimization in investment can be achieved by improved dynamism in construction management. The successful completion of coristruction projects within the specified programme, optimizing three major attributes of the process - quality, schedule and costs - has become the most valuable and challenging task for the engineer - managers to perform. So, the various aspects of construction management such as cost control, schedule control, quality assurance, management techniques etc. have also been discussed in this fifth chapter. Chapter 6 summarises the conclusions drawn from the above criticalr1 of rhajor construction projects in Kerala.

Performance Improvement of Fuzzy and Neuro Fuzzy Systems: Prediction of Learning Disabilities in School-age Children

Learning Disability (LD) is a classification including several disorders in which a child has difficulty in learning in a typical manner, usually caused by an unknown factor or factors. LD affects about 15% of children enrolled in schools. The prediction of learning disability is a complicated task since the identification of LD from diverse features or signs is a complicated problem. There is no cure for learning disabilities and they are life-long. The problems of children with specific learning disabilities have been a cause of concern to parents and teachers for some time. The aim of this paper is to develop a new algorithm for imputing missing values and to determine the significance of the missing value imputation method and dimensionality reduction method in the performance of fuzzy and neuro fuzzy classifiers with specific emphasis on prediction of learning disabilities in school age children. In the basic assessment method for prediction of LD, checklists are generally used and the data cases thus collected fully depends on the mood of children and may have also contain redundant as well as missing values. Therefore, in this study, we are proposing a new algorithm, viz. the correlation based new algorithm for imputing the missing values and Principal Component Analysis (PCA) for reducing the irrelevant attributes. After the study, it is found that, the preprocessing methods applied by us improves the quality of data and thereby increases the accuracy of the classifiers. The system is implemented in Math works Software Mat Lab 7.10. The results obtained from this study have illustrated that the developed missing value imputation method is very good contribution in prediction system and is capable of improving the performance of a classifier.

Reinforcement Learning Approaches to Power System Scheduling

One major component of power system operation is generation scheduling. The objective of the work is to develop efficient control strategies to the power scheduling problems through Reinforcement Learning approaches. The three important active power scheduling problems are Unit Commitment, Economic Dispatch and Automatic Generation Control. Numerical solution methods proposed for solution of power scheduling are insufficient in handling large and complex systems. Soft Computing methods like Simulated Annealing, Evolutionary Programming etc., are efficient in handling complex cost functions, but find limitation in handling stochastic data existing in a practical system. Also the learning steps are to be repeated for each load demand which increases the computation time.Reinforcement Learning (RL) is a method of learning through interactions with environment. The main advantage of this approach is it does not require a precise mathematical formulation. It can learn either by interacting with the environment or interacting with a simulation model. Several optimization and control problems have been solved through Reinforcement Learning approach. The application of Reinforcement Learning in the field of Power system has been a few. The objective is to introduce and extend Reinforcement Learning approaches for the active power scheduling problems in an implementable manner. The main objectives can be enumerated as:(i) Evolve Reinforcement Learning based solutions to the Unit Commitment Problem.(ii) Find suitable solution strategies through Reinforcement Learning approach for Economic Dispatch. (iii) Extend the Reinforcement Learning solution to Automatic Generation Control with a different perspective. (iv) Check the suitability of the scheduling solutions to one of the existing power systems.First part of the thesis is concerned with the Reinforcement Learning approach to Unit Commitment problem. Unit Commitment Problem is formulated as a multi stage decision process. Q learning solution is developed to obtain the optimwn commitment schedule. Method of state aggregation is used to formulate an efficient solution considering the minimwn up time I down time constraints. The performance of the algorithms are evaluated for different systems and compared with other stochastic methods like Genetic Algorithm.Second stage of the work is concerned with solving Economic Dispatch problem. A simple and straight forward decision making strategy is first proposed in the Learning Automata algorithm. Then to solve the scheduling task of systems with large number of generating units, the problem is formulated as a multi stage decision making task. The solution obtained is extended in order to incorporate the transmission losses in the system. To make the Reinforcement Learning solution more efficient and to handle continuous state space, a fimction approximation strategy is proposed. The performance of the developed algorithms are tested for several standard test cases. Proposed method is compared with other recent methods like Partition Approach Algorithm, Simulated Annealing etc.As the final step of implementing the active power control loops in power system, Automatic Generation Control is also taken into consideration.Reinforcement Learning has already been applied to solve Automatic Generation Control loop. The RL solution is extended to take up the approach of common frequency for all the interconnected areas, more similar to practical systems. Performance of the RL controller is also compared with that of the conventional integral controller.In order to prove the suitability of the proposed methods to practical systems, second plant ofNeyveli Thennal Power Station (NTPS IT) is taken for case study. The perfonnance of the Reinforcement Learning solution is found to be better than the other existing methods, which provide the promising step towards RL based control schemes for practical power industry.Reinforcement Learning is applied to solve the scheduling problems in the power industry and found to give satisfactory perfonnance. Proposed solution provides a scope for getting more profit as the economic schedule is obtained instantaneously. Since Reinforcement Learning method can take the stochastic cost data obtained time to time from a plant, it gives an implementable method. As a further step, with suitable methods to interface with on line data, economic scheduling can be achieved instantaneously in a generation control center. Also power scheduling of systems with different sources such as hydro, thermal etc. can be looked into and Reinforcement Learning solutions can be achieved.