Conceptual cost estimation of building projects with regression analysis and neural networks

Conceptual cost estimates play a crucial role in initial project decisions, although scope is not finalized and very limited design information is available during early project stages. In this paper, the advantages and disadvantages of the current conceptual cost estimation methods are discussed and the use of regression, neural network, and range estimation techniques for conceptual cost estimation of building projects are presented. Historical cost data of continuing care retirement community projects were compiled to develop regression and neural network models. Three linear regression models were considered to identify the significant variables affecting project cost. Two neural network models were developed to examine the possible need for nonlinear or interaction terms in the regression model. Prediction intervals were constructed for the regression model to quantify the level of uncertainty for the estimates. Advantages of simultaneous use of regression analysis, neural networks, and range estimation for conceptual cost estimating are discussed

Range estimation of construction costs using neural networks with bootstrap prediction intervals

Modeling of construction costs is a challenging task, as it requires representation of complex relations between factors and project costs with sparse and noisy data. In this paper, neural networks with bootstrap prediction intervals are presented for range estimation of construction costs. In the integrated approach, neural networks are used for modeling the mapping function between the factors and costs, and bootstrap method is used to quantify the level of variability included in the estimated costs. The integrated method is applied to range estimation of building projects. Two techniques; elimination of the input variables, and Bayesian regularization were implemented to improve generalization capabilities of the neural network models. The proposed modeling approach enables identification of parsimonious mapping function between the factors and cost and, provides a tool to quantify the prediction variability of the neural network models. Hence, the integrated approach presents a robust and pragmatic alternative for conceptual estimation of costs

Impact of occasional overtime on construction labor productivity: quantitative analysis

Scheduled and occasional overtime practices have been used frequently in the construction industry. Past research indicated that continuous scheduled overtime could have a negative effect on labor productivity. The impact of occasional overtime on productivity is generally expected to be less than the impact of scheduled overtime. However, few studies have evaluated the effects of occasional overtime on productivity, which is the main objective of this paper. Productivity data for 234 weeks were collected for quantitative analysis. The t test was performed initially to determine the statistical significance of the impact of occasional overtime. The assessment of productivity data samples revealed possible deviations from the normal distribution. The Wilcoxon rank sum test was implemented as an alternative to the t test. The results of quantitative analysis indicate that moderate levels of occasional overtime did not have a significant impact on productivity. Based on the findings in this study, the potential advantages of occasional overtime practices are discussed

Parametric Range Estimating of Building Costs Using Regression Models and Bootstrap

This paper presents a bootstrap approach for integration of parametric and probabilistic cost estimation techniques. In the proposed method, a combination of regression analysis and bootstrap resampling technique is used to develop range estimates for construction costs. The method is applied to parametric range estimation of building projects as an example. The bootstrap approach includes advantages of probabilistic and parametric estimation methods, at the same time it requires fewer assumptions compared to classical statistical techniques. This study is of relevance to practitioners and researchers, as it provides a robust method for conceptual estimation of construction costs

Üst-Sezgisel Yöntemlerle Kısıtlı Kaynaklı Birden Fazla Proje İçin İş Programı Hazırlanması

Proje kapsamında, kaynak kısıtlı orta ve büyük ölçekli projelerin ortak iş programlamasının ve kaynak atanmasının yapılması için üst-sezgisel algoritmaların kullanıldığı yöntemler geliştirilecektir. Çalışmada, grafik işleme birimli (GİB) tabanlı bilgisayar sistemlerinin paralel çalışma özellikleri kullanılarak üst- sezgisel algoritmaların hızlandırılması, ve ortak kaynak atama probleminin orta ve büyük ölçekli projeler için çözülmesi hedeflenmektedir

Discrete particle swarm optimization method for the large-scale discrete time-cost trade-off problem

Despite many research studies have concentrated on designing heuristic and meta-heuristic methods for the discrete time-cost trade-off problem (DTCTP), very little success has been achieved in solving large-scale instances. This paper presents a discrete particle swarm optimization (DPSO) to achieve an effective method for the large-scale DTCTP. The proposed DPSO is based on the novel principles for representation, initialization and position-updating of the particles, and brings several benefits for solving the DTCTP, such as an adequate representation of the discrete search space, and enhanced optimization capabilities due to improved quality of the initial swarm. The computational experiment results reveal that the new method outperforms the state-of-the-art methods, both in terms of the solution quality and computation time, especially for medium and large-scale problems. High quality solutions with minor deviations from the global optima are achieved within seconds, for the first time for instances including up to 630 activities. The main contribution of the proposed particle swarm optimization method is that it provides high quality solutions for the time-cost optimization of large size projects within seconds, and enables optimal planning of real-life-size projects

Pareto oriented optimization of discrete time cost trade off problem using particle swarm optimization

In project scheduling, it is feasible to reduce the duration of a project by allocating additional resources to its activities. However, crashing the project schedule will impose additional costs. Numerous research has focused on optimizing the trade-off between time and cost to achieve a set of non-dominated solutions. However, the majority of the research on time-cost trade-off problem developed methods for relatively simple problems including up to eighteen activities, which are not representing the complexity of real-life construction projects. In this work a Particle Swarm Optimization (PSO) technique is presented for Pareto oriented optimization of the complex discrete time-cost trade-off problems. The proposed PSO engages novel principles for representation and position-updating of the particles. The performance of the PSO is compared to the existing methods using a well-known 18-activity benchmark problem. A 63-activity problem is also included in computational experiments to validate the efficiency and effectiveness of the proposed PSO for a more complex problem. The results indicate that the proposed method provides a powerful alternative for the Pareto front optimization of DTCTPs

A SUPPORT VECTOR MACHINE METHOD FOR BID/NO BID DECISION MAKING

The bid/no bid decision is an important and complex process, and is impacted by numerous variables that are related to the contractor, project, client, competitors, tender and market conditions. Despite the complexity of bid decision making process, in the construction industry the majority of bid/no bid decisions is made informally based on experience, judgment, and perception. In this paper, a procedure based on support vector machines and backward elimination regression is presented for improving the existing bid decision making methods. The method takes advantage of the strong generalization properties of support vector machines and attempts to further enhance generalization performance by eliminating insignificant input variables. The method is implemented for bid/no bid decision making of offshore oil and gas platform fabrication projects to achieve a parsimonious support vector machine classifier. The performance of the support vector machine classifier is compared with the performances of the worth evaluation model, linear regression, and neural network classifiers. The results show that the support vector machine classifier outperforms existing methods significantly, and the proposed procedure provides a powerful tool for bid/no bid decision making. The results also reveal that elimination of the insignificant input variables improves generalization performance of the support vector machines