Ahmet Öztaş

University of Gaziantep, Ayıntap, Gaziantep, Turkey

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Publications (9)16.05 Total impact

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    ABSTRACT: The optimization of composite materials such as concrete deals with the problem of selecting the values of several variables which determine composition, compressive stress, workability and cost etc. This study presents multi-objective optimization (MOO) of high-strength concretes (HSCs). One of the main problems in the optimization of HSCs is to obtain mathematical equations that represents concrete characteristic in terms of its constitutions. In order to solve this problem, a two step approach is used in this study. In the first step, the prediction of HSCs parameters is performed by using regression analysis, neural networks and Gen Expression Programming (GEP). The output of the first step is the equations that can be used to predict HSCs properties (i.e. compressive stress, cost and workability). In order to derive these equations the data set which contains many different mix proportions of HSCs is gathered from the literature. In the second step, a MOO model is developed by making use of the equations developed in the first step. The resulting MOO model is solved by using a Genetic Algorithm (GA). GA employs weighted and hierarchical method in order to handle multiple objectives. The performances of the prediction and optimization methods are also compared in the paper.
    Expert Systems with Applications 04/2009; 36(3-36):6145-6155. DOI:10.1016/j.eswa.2008.07.017 · 1.97 Impact Factor
  • Önder Ökmen, Ahmet Öztaş
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    ABSTRACT: Schedules are the means of determining project duration accurately, controlling project progress, and allocating resources efficiently in managing construction projects. It is not sufficient in today's conditions to evaluate the construction schedules that are affected widely by risks, uncertainties, unexpected situations, deviations, and surprises with well-known deterministic or probabilistic methods such as the critical path method, bar chart (Gantt chart), line of balance, or program evaluation and review technique. In this regard, this paper presents a new simulation-based model-the correlated schedule risk analysis model (CSRAM)-to evaluate construction activity networks under uncertainty when activity durations and risk factors are correlated. An example of a CSRAM application to a single-story house project is presented in the paper. The findings of this application show that CSRAM operates well and produces realistic results in capturing correlation indirectly between activity durations and risk factors regarding the extent of uncertainty inherent in the, schedule.
    Journal of Construction Engineering and Management 01/2008; 134(1). DOI:10.1061/(ASCE)0733-9364(2008)134:1(49) · 0.87 Impact Factor
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    ABSTRACT: Quality Management Systems (QMS) are being operated in some sectors in Turkey but it is rare to meet these systems in construction industry. There are many hinderances that make it difficult to apply these systems effectively due to the nature of construction and therefore, no objective way of measuring the effectiveness of these systems exists in construction industry.This study aims to light the way for the studies and further researches in finding a way to measure the effectiveness of QMS. Two matrix models are developed as a way to measure the effectiveness of QMS. Towards this goal, firstly a questionnaire survey has been conducted to a sample of construction firms that have or have not passed through these systems in all over the Turkey. Appraising some findings from the survey results; the number of QMS operating firms and their way of implementing QMS principles are determined by using the most common statistical software ‘SPSS 10.0 for Windows’. These principles are evaluated on a case study by means of developing quality measurement matrices for QMS operating firms and different results have been concluded.
    Building and Environment 03/2007; 42(3):1219-1228. DOI:10.1016/j.buildenv.2005.12.017 · 2.70 Impact Factor
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    ABSTRACT: This study focuses on studying the effects of fly ash and silica fume replacement content on the strength of concrete cured for a long-term period of time by neural networks (NNs). Applicability of NNs to evaluate the effects of FA and SF for a long period of time is investigated. The investigations covered concrete mixes at different water cementitious materials ratio, which contained low and high volumes of FA, and with or without the additional small amount of SF. 24 different mixes with 144 different samples were gathered form the literature for this purpose. These samples consist concretes that were cured for 3, 7, 28, 56 and 180 days. A NN model is constructed trained and tested using these data. The data used in the NN model are arranged in a format of eight input parameters that cover the fly ash replacement ratio (FA), silica fume replacement ratio (SF), total cementitious material (TCM), fine aggregate (ssa), coarse aggregate (ca), water content (W), high rate water reducing agent (HRWRA) and age of samples (AS) and an output parameter which is compressive strength of concrete (fc). A NN program was devised in MATLAB and the NN model was constructed in this program. The results showed that NNs have strong potential as a feasible tool for evaluation of the effect of cementitious material on the compressive strength of concrete. It was found that FA content contributed little at early ages but much at later ages to the strength of concrete. It can also be concluded that the enhancement effect of low content of SF on compressive strength was not significant.
    Construction and Building Materials 02/2007; 21(2):384-394. DOI:10.1016/j.conbuildmat.2005.08.009 · 2.27 Impact Factor
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    ABSTRACT: High Strength Concrete (HSC) is defined as concrete that meets special combination of performance and uniformity requirements that cannot be achieved routinely using conventional constituents and normal mixing, placing, and curing procedures. HSC is a highly complex material, which makes modelling its behavior very difficult task. This paper aimed to show possible applicability of neural networks (NN) to predict the compressive strength and slump of HSC. A NN model is constructed, trained and tested using the available test data of 187 different concrete mix-designs of HSC gathered from the literature. The data used in NN model are arranged in a format of seven input parameters that cover the water to binder ratio, water content, fine aggregate ratio, fly ash content, air entraining agent, superplasticizer and silica fume replacement. The NN model, which performs in Matlab, predicts the compressive strength and slump values of HSC. The mean absolute percentage error was found to be less then 1,956,208% for compressive strength and 5,782,223% for slump values and R2 values to be about 99.93% for compressive strength and 99.34% for slump values for the test set. The results showed that NNs have strong potential as a feasible tool for predicting compressive strength and slump values.
    Construction and Building Materials 11/2006; 20(9):769–775. DOI:10.1016/j.conbuildmat.2005.01.054 · 2.27 Impact Factor
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    Ahmet Öztaş, Önder Ökmen
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    ABSTRACT: Various risks and uncertainties exist in construction projects. These may not only prevent the projects to be completed within budget and time limit, but also threaten the quality, safety and operational needs. In this context, risk analysis processes are the systematic methods to analyze the potential project risks and develop risk response strategies in order to cope with risks and achieve the desired objectives. This study proposes a new schedule risk analysis method named as judgmental risk analysis process (JRAP) and offers a different project duration equation through JRAP. The process (JRAP) can be defined as a pessimistic risk analysis methodology or a hypothesis based on Monte Carlo simulation that is effective in uncertain conditions due to its capability of converting uncertainty to risk judgmentally in construction projects. A case study has also been developed to show how the proposed process is applied on a construction project and to prove its validity.
    Building and Environment 09/2005; DOI:10.1016/j.buildenv.2004.10.013 · 2.70 Impact Factor
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    ABSTRACT: Total Quality Management (TQM) is considered by many researchers as an important quality and business performance improvement tool. The popularity of the concept has led to an explosion of TQM-related literature but there is a dearth of research and literature dealing with TQM implementation in the Turkish Cement Industry (TCI). In this study, the level of implementation of TQM philosophy in TCI is investigated. The paper describes the main findings through the critical factors of TQM, knowledge of TQM and perception of quality, data acquisition methods (operations, customer, employee, suppliers/vendors), and training. A questionnaire was sent to member firms of the Turkish Cement Manufacturers’ Association (TCMA). This paper presents a prescription for solving problems in the implementation of TQM to TCI.
    Total Quality Management and Business Excellence 09/2004; 15(7):985-999. DOI:10.1080/14783360410001681881 · 0.59 Impact Factor
  • Ahmet Öztaş, Önder Ökmen
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    ABSTRACT: Construction projects are being tendered and implemented under different contract systems and payment methods. Design–build has been a popular contract system in recent years. It provides various advantages through entailing the contractor carrying out and being responsible for not only construction but also the design of the work. However, design–build turns out to be a risky system for both owners and contractors unless the risks are identified, analyzed and managed throughout the tender preparation and project execution stages. In this context, this study aims to present a literature survey on the issues of risk, risk management/analysis and the design–build contract system, to propose a schedule and cost risk analysis model, and to show the applicability of these models in scheduling and cost estimation of a fixed-price design–build construction project through a case study.
    Building and Environment 02/2004; DOI:10.1016/j.buildenv.2003.08.018 · 2.70 Impact Factor