Particle Swarm Optimization Based Approach for Estimation of Costs and Duration of Construction Projects

Cost and duration estimation is essential for the success of construction projects. The importance of decision making in cost and duration estimation for building design processes points to a need for an estimation tool for both designers and project managers. Particle swarm optimization (PSO), as the tools of soft computing techniques, offer significant potential in this field. This study presents the proposal of an approach to the estimation of construction costs and duration of construction projects, which is based on PSO approach. The general applicability of PSO in the formulated problem with cost and duration estimation is examined. A series of 60 projects collected from constructed government projects were utilized to build the proposed models. Eight input parameters, such as volume of bricks, the volume of concrete, footing type, elevators number, total floors area, area of the ground floor, floors number, and security status are used in building the proposed model. The results displayed that the PSO models can be an alternative approach to evaluate the cost and-or duration of construction projects. The developed model provides high prediction accuracy, with a low mean (0.97 and 0.99) and CoV (10.87% and 4.94%) values. A comparison of the models’ results indicated that predicting with PSO was importantly more precise

Early Stage Construction Cost Prediction in Function of Project Sustainability

Construction project costs often reach values higher than planned. Accuracy in project cost estimation is one of the most important criteria for project success, even for its sustainability.The main idea of this research is to examine the relationship between realized cost and contracted cost values for residential buildings. The aim of the research is to determine the mathematical relationship between realized and planned costs in the project implementation phase by using a few mathematical methods and some machine learning methods in comparison to linear regression. This would enable validation of methods themselves by comparing and evaluating the obtained relevant parameters.Comparison would be performed on two levels, based on its general characteristics, as well as on the results of their application on the basis of 24 building reconstructions and new buildings by comparing the mean absolute percentage error (MAPE) and the determination coefficient (R2) using Predictive Modelling Software DTREG (pronounced D-T-Reg). The relationship of realized and planned costs will be determined for the building as a whole and for certain types of construction works. That relationship would enable more realistic budget planning of similar future projects. Cost overrun factors will be analysed for particular types of construction works, as well as the probability of their occurrence, and what measures should be undertaken to prevent or reduce them in similar future projects. The phenomenon known in project planning as "optimism bias" will be analysed in the context of research focus of exceeding the construction cost

Guess the time of implementation of residential construction projects using neural networks ANN

The construction duration of residential projects, especially in building processes, significantly impact the business of a construction company. The balance between the planned cost, direct cost, and overheads directly depend on the precision of the implementation phase of the project. The application of the artificial neural network (ANN) to predict the duration of implementation of a residential construction project from the pre-design stage to completion is comprehensively discussed in this research. The study applies the back-propagation (BP) network made of nodes for error evaluation of the training states. Further, the proposed system illustrated that the artificial neural network (ANN) satisfy the three crucial criteria (cost, quality, and time) used for the evaluation of projects. The ANN provided accurate data for the training and estimation of, the duration of a residential construction project with adequate resources of implementation. The performance of the results for the ANN at 105 iteration shows that the prediction was 99.841% accurate for the overall system. The best fit occurred at the 99th epoch with an MSE of 0.10286

Quantifying Critical Success Factors (CSFs) in Management of Investment-Construction Projects: Insights from Bayesian Model Averaging

The problem with evaluating investment projects is that there are many factors that determine the degree of their successful conclusion. Consequently, there has been an active debate for years as to which critical success factors (CSFs) contribute most to the performance of construction projects. This is because the practice of empirical research is based on two steps: first, researchers choose a particular model from the space of all possible models, and second, they act as if the chosen model is the only one that fits the data and describes the phenomenon under study. Hence, there are many CSF lists that can be found in the literature, owing to the uncertainty at the model selection stage, which is usually ignored. Alternatively, model averaging accounts for this model uncertainty. In this study, the Bayesian model averaging and data from a survey of Polish construction managers were used to investigate the potential of 28 factors describing a diverse set of characteristics in explaining the performance of construction projects in Poland. Determinants of successful completion of investment projects are categorized by their level of evidential strength, which is derived from posterior inclusion probabilities (PIPs), i.e., providing strong, medium and weak evidence

ANN Based Approach for Estimation of Construction Costs of Sports Fields

Cost estimates are essential for the success of construction projects. Neural networks, as the tools of artificial intelligence, offer a significant potential in this field. Applying neural networks, however, requires respective studies due to the specifics of different kinds of facilities. This paper presents the proposal of an approach to the estimation of construction costs of sports fields which is based on neural networks. The general applicability of artificial neural networks in the formulated problem with cost estimation is investigated. An applicability of multilayer perceptron networks is confirmed by the results of the initial training of a set of various artificial neural networks. Moreover, one network was tailored for mapping a relationship between the total cost of construction works and the selected cost predictors which are characteristic of sports fields. Its prediction quality and accuracy were assessed positively. The research results legitimatize the proposed approach

4º congresso português de ‘Building Information Modelling’ vol. 2 - ptBIM

Livro de atas do Congresso ptBIM 2022, onde se promove um fórum de discussão técnico-científica em língua Portuguesa nas metodologias ‘Building Information Modelling’ (BIM), envolvendo a participação ativa das comunidades profissional e académica das áreas de Arquitetura e Engenharia. Pretende-se enfatizar os problemas e esforços de implementação na Indústria da Construção e reforçar as redes de profissionais que incorporam práticas BIM nas suas atividades. https://ptbim.org

4º congresso português de ‘Building Information Modelling’ vol. 2 - ptBIM

PublishedLivro de atas do Congresso ptBIM 2022, onde se promove um fórum de discussão técnico-científica em língua Portuguesa nas metodologias ‘Building Information Modelling’ (BIM), envolvendo a participação ativa das comunidades profissional e académica das áreas de Arquitetura e Engenharia. Pretende-se enfatizar os problemas e esforços de implementação na Indústria da Construção e reforçar as redes de profissionais que incorporam práticas BIM nas suas atividades. https://ptbim.org