Novel machine-learning model for estimating construction costs considering economic variables and indexes

In addition to materials, labor, equipment, and method, construction cost depends on many other factors such as the project locality, type, construction duration, scheduling, and the extent of use of recycled materials. Further, the fluctuation of economic variables and indexes (EV&Is), such as liquidity, wholesale price index, and building services index, causes variation in costs. These changes may increase or reduce the construction cost, are hard to predict, and are normally ignored in the traditional cost estimation computation. This paper presents an innovative construction cost estimation model using advanced machine-learning concepts and taking into account the EV&Is. A data structure is proposed that incorporates a set of physical and financial (P&F) variables of the real estate units as well as a set of EV&Is variables affecting the construction costs. The model includes an unsupervised deep Boltzmann machine (DBM) learning approach along with a softmax layer (DBM-SoftMax), and a three-layer back-propagation neural network (BPNN) or another regression model, support vector machine (SVM). The role of DBM-SoftMax is to extract relevant features from the input data. The role of the BPNN or SVM is to turn the trained unsupervised DBM into a supervised regression network. This combination improves the effectiveness and accuracy of both conventional BPNN and SVM. A sensitivity analysis was performed within the algorithm in order to achieve the best results taking into account the impact of the EV&I factors in different times (time lags). The model was verified using the construction cost data for 372 low- and midrise buildings in the range of three to nine stories. Cost estimation errors of the proposed model were much less than those of both the BPNN-only and SVM-only models, thus demonstrating the effectiveness of the strategies employed in this research and the superiority of the proposed model.