Using machine learning techniques for early cost prediction of structural systems of buildings

Thesis (Doctoral)--İzmir Institute of Technology, Architecture, İzmir, 2005Includes bibliographical references (leaves:111)Text in English; Abstract: Turkish and Englishx, 111 leavesIt is desirable to predict construction costs in the early design stages in order tomake sure that target costs are met and competitive prices are realized. This study investigates the possibility of predicting the cost of construction early in the design phase by using machine learning (ML) techniques. To achieve this objective, artificialneural network (ANN) and case based reasoning (CBR) prediction models were developed in a spreadsheet-based format. An investigation of the impacts of weight generation methods on the ANN and CBR models was conducted. The performance of the ANN model was enhanced by experimenting with the weight generation methods of simplex optimization, back propagation training, and genetic algorithms while the CBR model was augmented by feature counting, gradient descent, genetic algorithms (GA), decision tree methods of binary-dtree, info-top and info-dtree.Cost data belonging to the superstructure of low-rise residential buildings were used to test these models. It was found that both approaches were capable of providing high prediction accuracy, 96% for ANN using simplex optimization for weight determination, and 84% for CBR using GA for attribute weight selection. A comparison of the Excel-based ANN and CBR models was made in terms of prediction accuracy, preprocessing effort, explanatory value, improvement potentials and ease of use. The study demonstrated the practicality of using spreadsheets in developing ANN and CBR models for use in construction management as well as the potential benefits of enhancing ANN and CBR models by using different weight generation methods

The Prediction of Low-Rise Building Construction Cost Estimation Using Extreme Learning Machine

This study aims to predict the possibility of low-rise building construction costs by applying machine learning models, and the performance of each model is evaluated and compared with ensemble methods. The artificial neural network (ANN) emerges as the top-performing individual model, attaining an accuracy of 0.891, while multiple linear regression and decision trees follow closely with accuracies of 0.884 and 0.864 respectively. Ensemble methods like maximum voting ensemble (MVE) improve the accuracy beyond individual models with an impressive accuracy rate of 0.924. Meanwhile, the stacking ensemble and averaging ensemble also demonstrate competitive performance with accuracies of 0.883 and 0.871, respectively. These findings can result in more informed decision-making, which is valuable for the real estate industry

State-of-the-art on research and applications of machine learning in the building life cycle

Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science

Comparison of Various Machine Learning Models for Estimating Construction Projects Sales Valuation Using Economic Variables and Indices

The capability of various machine learning techniques in predicting construction project profit in residential buildings using a combination of economic variables and indices (EV&Is) and physical and financial variables (P&F) as input variables remain uncertain. Although recent studies have primarily focused on identifying the factors influencing the sales of construction projects due to their significant short-term impact on a country's economy, the prediction of these parameters is crucial for ensuring project sustainability. While techniques such as regression and artificial neural networks have been utilized to estimate construction project sales, limited research has been conducted in this area. The application of machine learning techniques presents several advantages over conventional methods, including reductions in cost, time, and effort. Therefore, this study aims to predict the sales valuation of construction projects using various machine learning approaches, incorporating different EV&Is and P&F as input features for these models and subsequently generating the sales valuation as the output. This research will undertake a comparative analysis to investigate the efficiency of the different machine learning models, identifying the most effective approach for estimating the sales valuation of construction projects. By leveraging machine learning techniques, it is anticipated that the accuracy of sales valuation predictions will be enhanced, ultimately resulting in more sustainable and successful construction projects. In general, the findings of this research reveal that the extremely randomized trees model delivers the best performance, while the decision tree model exhibits the least satisfactory performance in predicting the sales valuation of construction projects

Construction cost estimation of reinforced and prestressed concrete bridges using machine learning

U ovom radu istraženo je sedam najnovijih postupaka strojnog učenja za procjenu troškova izgradnje armiranobetonskih i prednapetih betonskih mostova, uključujući umjetne neuronske mreže (ANN) i ansamble ANN, ansamble regresijskih stabala (eng. random forests, boosted and bagged regresijska stabla), metodu potpornih vektora za regresiju (SVR) i Gausov regresijski proces (GPR). Stvorena je i baza podataka o troškovima izgradnje i projektnim karakteristikama za 181 armiranobetonski i prednapeti betonski most za treniranje i ocjenu modela.Seven state-of-the-art machine learning techniques for estimation of construction costs of reinforced-concrete and prestressed concrete bridges are investigated in this paper, including artificial neural networks (ANN) and ensembles of ANNs, regression tree ensembles (random forests, boosted and bagged regression trees), support vector regression (SVR) method, and Gaussian process regression (GPR). A database of construction costs and design characteristics for 181 reinforced-concrete and prestressed-concrete bridges is created for model training and evaluation

A review of data-driven building performance analysis and design on big on-site building performance data

Building performance design (BPD) is a crucial pathway to achieve high-performance buildings. Previous simulation-based BPD is being questioned due to the performance gaps between simulated and measured values. In recent years, accumulated on-site building performance data (OBPD) make it possible to analyze and design buildings with data-driven methods. This article makes a review of previous studies that conducted data-driven building performance analysis and design on a large amount of OBPD. The covered studies are summarized by the applied techniques, i.e., statistics, regression, classification, and clustering. The data used by these studies are compared and discussed emphasizing the data size and public availability. A comprehensive discussion is given about the achievements of existing studies, and challenges for boosting data-driven BPD from three aspects, i.e., developing data-driven models, the availability of building performance data, and stimulation of industrial practices. The review results indicate that data-driven methods were commonly applied to estimate energy consumptions, and explore energy trends, determinant features, and reference buildings. Identifying determinant features is one of the most successful applications. This study highlights the future research gaps for boosting data-driven building performance design

A Review of Metrics and Modeling Techniques in Software Fault Prediction Model Development

This paper surveys different software fault predictions progressed through different data analytic techniques reported in the software engineering literature. This study split in three broad areas; (a) The description of software metrics suites reported and validated in the literature. (b) A brief outline of previous research published in the development of software fault prediction model based on various analytic techniques. This utilizes the taxonomy of analytic techniques while summarizing published research. (c) A review of the advantages of using the combination of metrics. Though, this area is comparatively new and needs more research efforts