A model for early construction cost prediction is useful for all construction project participants. This paper presents a combination of process‐based and data‐driven model for construction cost prediction in early project phases. Bromilow’s “time‐cost” model is used as process‐based model and general regression neural network (GRNN) as data‐driven model. GRNN gave the most accurate prediction among three prediction models using neural networks which were applied, with the mean absolute percentage error (MAPE) of about 0.73% and the coefficient of determination R² of 99.55%. The correlation coefficient between the predicted and the actual values is 0.998. The model is designed as an integral part of the cost predicting system (CPS), whose role is to estimate project costs in the early stages. The obtained results are used as Cost Model (CM) input being both part of the Decision Support System (DSS) and part of the wider Building Management Information System (BMIS). The model can be useful for all project participants to predict construction cost in early project stage, especially in the phases of bidding and contracting when many factors, which can determine the construction project implementation, are yet unknown.

Bitni element svakog ugovora za građevinski projekt su troškovi kao bitni element pa točnost predviđanja troškova može utjecati na izvršenje projekta, a također i na poslove onih koji su uključeni u projekt. Podaci za objekte...

The need of respecting the construction time as one of the construction contract elements points out that early prediction of construction time is of crucial importance for the construction project participants’ business. Thus, having a model for early prediction of construction time is useful not only for the participants involved in the construction contracting process, but also for other participants in the construction project realization. Regarding that, this paper aims to present a hybrid method for predicting construction time in the early project phase, which is a combination of process‐based and data‐driven models. Five hybrid models have been developed, and the most accurate one was the BTC‐GRNN model, which uses Bromilow’s time‐cost (BTC) model as a process‐based model and the general regression neural network (GRNN) as a data‐driven model. For evaluating the quality of the models, the 10‐fold cross‐validation method has been used. The mean absolute percentage error (MAPE) of the BTC‐GRNN is 3.34% and the coefficient of determination R², which reflects the global fit of the model, is 93.17%. These results show a drastic improvement of the accuracy in comparison to the model when only data‐driven model (GRNN) has been used, where MAPE was 31.8% and R² was 75.64%. This model can be useful to the investors, the contractors, the project managers, and other project participants for construction time prediction in the early project phases, especially in the phases of bidding and contracting, when many factors, that can determine the construction project realization, are unknown.

PurposeIntegrating the aspects of sustainability into facilities design has become a designers’ challenge, and the early design phase is seen as the most important in implementing sustainability into facilities design. Therefore, this paper aims to analyze the factors that influence sustainability assessment of preliminary design of facilities and predicts sustainability assessment depending on those factors. Design/methodology/approachData were collected by survey questionnaire distributed to project managers using a six-point Likert scale. Obtained data were modeled with general regression neural network (GRNN) using DTREG software. In total, 27 factors were chosen for determining the most accurate predictive model, and their importance was computed. FindingsThe six most important factors for sustainability assessment of facilities design are: work experience, work on several outline design proposals, resolving issues between stakeholders, prioritization of participants in the design phase, procurement management and defining projects’ program and goals. The predictive model that was used for prediction of the sustainability assessment was shown to be highly accurate, with MAPE (mean absolute percentage error) amounting to 2.58 per cent. Practical implicationsUsing the same approach, assessment of every other factor for the preliminary design can be predicted and the factors that are most influential to its sustainability can be obtained. Originality/valueThe paper supports the sustainability improvement of the preliminary design of future facilities’ projects, as well as support during the decision-making process.

Incorporating waste tyre rubber into cement composites could pose a sustainable strategy for reducing tyre waste, whilst simultaneously preserving natural resources. Accurate prediction of the compressive strength is essential for the efficient development and application of the new material. This study presents the experimental research and a new proposed machine learning modelling framework aimed at predicting the compressive strength of conventional and rubber-modified cement composites containing recycled rubber derived from end-of-life vehicle tyres. Experimental datasets from compressive strength tests on 48 concrete and 40 mortar specimens were used to develop predictive models, with varying rubber content. The empirical results show that increasing the percentage of waste rubber in the mixture designs negatively affects compressive strength, reducing it up to 96.06% for mortars, up to 64.4% for concrete made with ordinary Portland cement, and up to 60.7% for concrete made with sulfate-resistant cement. Three soft computing techniques were implemented using the DTREG predictive modelling software: the General Regression Neural Network (GRNN), Radial Basis Function Neural Network (RBFNN), and Support Vector Machine (SVM). The conventional linear regression (LR) model was also implemented and compared with these three machine learning models. They were trained using the experimentally measured results for the various mixtures. The model performance was evaluated using the standard estimators: the coefficient of determination (R²), correlation coefficient (r), and mean absolute percentage error (MAPE). The GRNN demonstrated the best predictive abilities for predicting the strength of mortars, with MAPE = 4.6% and R² = 99.27% on the original dataset; on the normalized dataset, MAPE was 3.9%, and R² was 99.65. For predicting the strength of concrete, GRNN also outperformed the other models on the original dataset with MAPE = 3.8% and R² = 98.4%. The rubber content was identified as the most influential parameter affecting the compressive strength, while density, admixture conditions, the water-cement ratio, and curing age also contributed to the model’s performance. The LR model presented the lowest predictive accuracy among all evaluated algorithms (models) because of the significant complex nonlinear interactions between predictors and target, which can not be adequately modelled with a linear framework. Within the investigated experimental domain, the developed models showed high predictive accuracy and can be used for preliminary strength estimation and interpolation for mixtures with similar characteristics.