1. Introduction
With the growing demand for green buildings worldwide, it has become necessary to develop a new adequate research field to create practical evaluation approaches for green building bidders to guarantee that the selected bid winner has practical experience and knowledge of all the required stages, which are vital to finish such projects with the required time, cost, quality, safety, and environmental aspects. In addition, influential research ensures the ability to successfully leave space for the green construction industry to cope with emerging technologies [
1]. Thus, accurate construction cost prediction models need to consider all influential attributes. Considering that evaluating the cost of traditional buildings is utterly different from that of green buildings designed to be environmentally friendly through the production of zero greenhouse gases, it is necessary to design cost forecasting models differently and innovatively [
2,
3,
4]. Although the current literature contains various cost prediction models for traditional buildings, minimal efforts have been directed toward green building cost estimation modeling. Green construction cost biddings contain more detailed requirements when compared to traditional ones. Having such detailed comprehensiveness makes it complicated for stakeholders to use the rule of thumb for cost estimation [
5]. Consequently, various indicators should be considered when selecting the bid winner, including green building management, environmental aspects, material use, water use and water protection, land and construction site protection, and energy use.
The commonly adopted procedure in bid winner selection is the lowest price. Such an approach may be adopted with minimal adverse impact on traditional building construction, yet it is hazardous to employ such an approach for green building construction bidding awards. Thus, improving the policy of the public and private sectors for selecting bid winners is crucial for maximizing construction quality and the added value [
6]. Furthermore, adopting robust and accurate cost prediction methodologies is expected to strengthen the competition between the bidders and enhance the awarding process to ensure successful green construction. Therefore, effective construction cost forecasting has positive practical implications on economic, social, and environmental levels.
The inability to appropriately manage the selection of the bid winner results in significant delays in project delivery; hence, the bid winner must be carefully picked [
7]. The current study opens up new possibilities for developing innovative and integrated models for green building cost prediction that consider various influencing factors and may be used in the bidding awarding procedures to reduce the financial and legal conflicts among contractual parties for such projects. Construction project delivery techniques are critical in forecasting bidding costs and, accordingly, in updating the bid winner selection policy by adhering to regulations, assizes, and guidelines to avoid patronage. Consequently, in forecasting the total cost of green building construction projects, it is challenging to establish a suitable balance in the bid cost in anticipation of the actual cost. As a result, a new method incorporating the primary aspects influencing the cost of green building construction should be developed. In this study, a machine learning-based model for predicting green building construction costs was developed, which is critical for paving the way to selecting the best bidder to fulfill the required conditions, expanding the benefits of green construction, and accommodating rapid changes along with future change orders.
2. Literature Review
Green buildings are designed to meet current and future generations’ needs in protecting planet Earth. Since the nineteenth century, the demand for such a green construction approach has become necessary for efficient pollution reduction, dynamic use of materials, and more social inclusiveness by reducing the ripple effect of the construction process and maintaining environmentally friendly building operations [
8]. Several green building design approaches have been proposed to simplify green building deliveries (e.g.,
and the Building Research Establishment Environmental Assessment Method). For instance, in 2021,
-certified green buildings were up to over 100,000 in the United States and 69,800 in Canada [
9,
10]. Public construction biddings are part of the public procurement structure that seizes a significant portion of the public expense yearly, which is estimated to be about ten percent of total incomes in most countries in North America [
11].
In most traditional bids, the winner is determined based only on the bid price aspect following the law, which is called the lowest-priced bid. If such a winner cannot perform the bid, the bid is awarded directly to the second-lowest price [
12]. This is the second type of bidding adopted in the market. This approach increases the bid price compared to the lowest-bid approach [
13]. These approaches are impractical for the owner as all bidders try to make their estimate lower than others and lower the actual cost to win the bid [
14]. Most private and public firms adopt such an approach in bidding in the United States and European countries [
15,
16]. However, applying the lowest price approach in bidding creates various issues regarding work quality, construction delays, disputes, and claims [
17,
18].
Though the lowest price approach is the best for cost reduction purposes and is widely used in the construction industry, it is a recipe for conflict, primarily when intense competition exists [
19,
20,
21]. For instance, in Turkey, most contractors who have won construction bids via a lower-price approach have faced trouble in the construction project delivery. In one study, about 430 questionnaires were analyzed after being sent to contractors in Turkey. The results indicated that such consequences have occurred because contractors are trying to continue in the market regardless of inaccurate bidding and little experience in contract pricing [
22].
Change order costs also increase when the lowest-price approach is adopted, especially in green building construction. In addition, the “multiattribute” is used at the best price in the construction industry to achieve the best value [
23,
24]. Another approach utilized is the average bid, where the winner is selected by applying a single criterion. First, the price is compared with the average of all the bidders’ prices and then the one that is closest to the average bid is adopted [
25,
26,
27]. The Peruvian approach has also been applied to construction project bids by removing outliers’ bid values that have a price increase or decrease of 10% of the actual average bid price from the submitted list. The new average is determined in the next step to choose the bidder that meets both conditions closest to and below the new average price [
28]. Bidders are classified based on several aspects, such as quality, profitability, leverage, and expertise, to determine who is eligible to win the bid. A neural network technique was employed in the same vein to decide on such a concept [
29]. In addition, several experimental methods have been used for bidding selection. For instance, the non-competitive method was implemented in construction bids, and various identification strategies were evaluated experimentally [
30]. Additionally, an analytical model was developed based on game theory to address construction project claims and opportunistic bidding [
26].
Moreover, risk possibility and competition for projects were measured using a case-based reasoning method [
31]. Using general regression and classification networks, choosing the fittest bid closest to the actual construction project price was studied [
15]. The bid scoring formula is a realistic approach to selecting the winner and is still valid for providing a promotional result that meets the needs of the owner and contractor [
32].
Green building bids are converting from traditional contracts in choosing a winner to smarter contracts that meet all the sustainability requirement changes in public bidding [
33,
34]. The design-build (
) approach has been used in green and traditional buildings. It maintains a high integration of the design stages for sustainability in green buildings [
5].
is a practical approach to incentivize bidders and make them more familiar with maintaining sustainable goals during the construction and design processes [
35,
36,
37]. The dynamic and practical connection between the contractor and the design group is a significant feature of improving the concept of all aspects of green buildings, such as quality, cost, and time [
33,
38,
39]. Compared to the traditional approach, such as the low-bid approach to choosing a winner,
seems to have significant advantages for sustainable green building objectives [
40,
41]. Many approaches have been improved since the 1990s to find a rating approach to handle green buildings properly [
42]. To achieve a comprehensive vision of all aspects of the bid details and to meet the required conditions written in the contract to satisfy all parties, when determining the bid winner, it is vital to consider the quality, environment, technical aspects, reputation, and price. This approach ensures that more work and effort is put into enforcing adequate and acceptable criteria for choosing the winner to attain the optimal advantage [
16,
41,
42]. Many factors must be considered for the conceptual phase in the design and planning stages of the green building to acquire practical work [
43]. In addition, effective green building design must employ efficient natural and ecologic resource management processes [
44]. To fulfill such a need, selecting the best bidder is required.
4. ML Model Results
4.1. Experimental Setup
The K-fold cross-validation process was applied to develop the model’s accuracy by examining the
algorithm performance on various datasets. In addition, the model hyperparameters were tuned using a K-fold cross-validation technique. First, the database must be separated into subsets for training and testing the
modeling process. During this process, the training dataset is partitioned into multiple ‘k’ smaller portions. The term ‘K-fold’ was coined as a result. Then, testing is done with K-fold, while training is done with k-1. Both are also based on a random dataset. In addition, the model hyperparameters are tuned using a K-fold cross-validation technique. The prediction model is then fitted to the training set using the best possible hyperparameter configuration. Consequently, each fold is only utilized as a validation set once. Finally, the accuracy measures for each fold may be compared, and if they are similar, the model is likely to generalize well, as shown in
Figure 8.
4.1.1. Hyperparameter Optimization
The hyperparameters of the proposed
algorithms used in this study had to be tuned, as shown in
Table 4. These hyperparameters were modified depending on the actual dataset rather than the manual determinations. Thus, the investigation was carried out with k = 1 to k = 10 for K-fold cross-validation. Each
represents the grid search for the optimum
model selection and hyperparameter tuning. As a result, five-fold cross-validation had the best prediction accuracy, as discussed the in the performance evaluation.
4.1.2. Feature Importance Analysis
Pearson’s correlation process was conducted between the selected features and the cost values of green building projects to assess the influence of these features on each other and the observed values, as presented in
Figure 9.
Better insight and understanding of the model’s features help decision-makers plan and formulate policies effectively. As a result, a feature importance process was carried out using the
,
, and
techniques to identify the importance degree of each feature included in forecasting green building costs. As illustrated in
Figure 10, the feature scale plot was implemented to calculate a relative score for each variable. In addition, as presented in
Figure 10, the features were ranked in descending order of importance: people, technological, technical, and specific requirements.
4.2. Performance Evaluation
After testing the primary model assumptions, it was vital to evaluate the suggested models’ usefulness and predictive capability. Thus, the assessment measurements were utilized to evaluate the proposed models’ proficiency. Four statistical measures (i.e.,
,
,
, and
) were employed to investigate the efficiency of the suggested
models, as presented in Equations (5)–(9).
where
symbolizes the actual (measured) values of the overstrength ratio of short links,
symbolizes the forecasted outcome,
symbolizes the mean of the
,
symbolizes the number of datasets utilized, and
is an independent variable. The model accuracy is increased if the
value approaches 1 and the
,
, and
values approach 0. A set of random, nonoverlapping partitioned folds were used as training and test datasets for k = 3, k = 5, and k = 7, together with their corresponding performance measures. Therefore, the effectiveness of the suggested
models was assessed utilizing a stratified five-fold cross-validation technique, as shown in
Table 5.
The comparison of the efficiency of
algorithms (i.e.,
) to predict the cost of the green building projects was implemented for
. Accordingly, the results of the evaluation measures were computed, as shown in
Table 6.
The comparison outcomes show that
and
had higher
values (more than 0.90) and lower
,
,
values than the
model in predicting green building costs. All assessment metrics results also revealed that
had excellent prediction capability and had the highest
value. Furthermore,
had the lower values for the rest of the evaluation metrics (i.e.,
,
, and
) compared to the
model, as shown in
Figure 11. Moreover, the forecasted outputs of the
model illustrate that its prediction values were very close to the values of green building project costs. It is worth noting the progress of the
estimate for each developed model. The
’s decisive
value was 0.96, implying that the
model was somewhat mounted to the datasets since it was close to 1. Given that the forecast
was considerably superior to the regular
, this means that the
model did forecast new interpretations and fit the existing dataset. Therefore, the
model had a better fit and slight deviation from the actual values of the green building costs. Consequently, the
was the most effective and competent model for predicting green building costs.
4.3. Experimental Results
The current study was designed to expand and augment the literature in forecasting green building costs. First, the four main features that affect the cost of green buildings were thoroughly investigated and disaggregated into its primary sub-attributes. Then these factors were evaluated according to their acquired data record by developing machine learning-based prediction models in combination with accuracy evaluation matrices to focus on the uncertainty coupled with the cost forecasting. The current research findings reveal that people primarily affect green building cost features, followed by technological, technical, and special requirements, which implies that spreading the “green” culture amongst involved personnel is critical to minimizing the construction cost. In addition, green building contractors need to utilize cutting-edge technologies that can facilitate the deployment of efficient technical approaches necessary for cost objective optimization purposes, reflecting the importance of applying prediction models to produce accurate cost predictions. In line with the adopted methodology, the results reveal the significance of examined attributes and their sub-categories, such as people, technological, technical, and other specific requirements, which demonstrates the consequence of changes in the cost objective functions of alternatives. For example, the cost function was an average of 88% compared to the people, technological, technical, and special requirements at 93%, 90%, 96%, and 82%. Furthermore, variation in the cost objective function was analyzed through various sub-attributes, implying a high dependency and influencing the proficiency of the decision-making process.
Consequently, the cost prediction models utilize the cost-effective frontline approach to affect this matter and streamline the proper decision-making process. The established cost prediction models showed that outperformed the and by and , respectively. Thus, the prediction model represents the most attractive alternative for decision-makers from both an economical and sustainable point of view for the most accurate prediction with the lowest cost objective function and lowest correlated risk.
5. Discussion
This study’s cost prediction models offer an insightful perception of the correlation between influential features and green buildings’ green cost premium. The cost of each building was forecasted via up-to-date machine learning approaches to reflect the cost function variation based on datasets recovered from 283 green building projects that were examined in North America. The proposed models can be used by green building vendors, designers, stakeholders, and decision-makers to predict the green cost objective function of their new green buildings based on the characteristics of the main influential factors. It ought to be stated that the scope of the current research is partial to green buildings in the United States. It is also limited in the quantity of the collected data and the number of the main attributes considered. Thus, it is critical to consider the impact of governmental and non-governmental external support.
It should also be explained that the current research was restricted to economic and sustainable assessments and did not consider social and environmental dimensions. Additionally, the emphasis of the current research was on comparing the predicted cost objective function against the actual construction cost without considering the construction life cycle cost analysis elements or the reimbursement cycle. Therefore, further research is required to combine the current research with additional data, more attributes, various green buildings, and recently -accredited green buildings.
Decision-makers are increasingly relying on technological findings to upgrade and build policies. The current study develops a robust machine learning framework for predicting green building construction costs from recorded datasets, which can be utilized as a general model to simulate all associated characteristics. This provides a good foundation for investigating how different feature interconnectivity might share insight on green building construction cost forecasting. Furthermore, as contemporary machine learning algorithms grow, increasingly sophisticated forecast models provide ways to develop more valuable and exact modeling for green building construction cost prediction, which many construction business practitioners may then use. Finally, consistent with what is currently emerging in the construction engineering and management research fields, the authors are confident in the proposed models’ ability to provide stakeholders with more precise forecasts to fit accessible datasets as advantageous preceding information to feed machine learning-based models.
The current research aims to minimize the knowledge gap in predicting green building costs. Thus, the proposed models were designed after an intensive investigation of the currently available related models. For example, one of the main gaps is the lack of an integrated representation of how the main attributes affect the green building cost interconnectedly. One of the main issues with the already available green building cost prediction models is that they lack integrity. Not considering all of the influential features of green building costs has ripple effects on the developed model’s dependability. For example, some studies have focused on the features of green building technologies while relaxing the people, technical, and specific requirements [
61]. This has a detrimental impact on the accuracy of green building cost forecasts since a high level of uncertainty frequently accompanies the major qualities. In addition, little effort has been put into establishing analytical or machine learning-based models for green construction cost prediction. Many existing models use survey and questionnaire methodologies to describe the practice case for green building costs. There is always a need for more quantitative and objective techniques for projecting green construction costs [
3,
61].
Some studies have provided models that partially forecast the cost of only green building-certified residences [
2]. In addition, other research papers have focused on the green certification of office buildings and the cost of equity capital of green buildings [
4,
62]. Consequently, a holistic model for green building cost estimation is required to provide reliable forecasting tools for practitioners.
Furthermore, historical green construction cost data are scarce. The established data gathering processes and dataset comprehensiveness have also been a significant impediment for researchers in developing reliable and general prediction models, especially for large-budget building contracts. As a result, building a prediction model that can be used effectively and independently of the construction cost value is critical. Several academics have attempted to anticipate green construction costs; however, their conclusions were limited to a single location since only a few relevant features were evaluated [
2,
3,
63]. Such problems prove that decision-making tools are in great demand in the construction industry [
64,
65,
66,
67,
68,
69].
The contradictory findings hampered the assessment of green construction costs, making this a worrying problem. Furthermore, to the best of the authors’ knowledge, no appropriate model for predicting green construction costs is available in the present literature. As a result, a general and worldwide model for green cost prediction is required. The created green building cost prediction models have an advantage over comparable modeling techniques available in the literature because of their different processing chronological sequence, where forecasts are less impacted by the number of classes and can be analyzed consistently. The created models produce fewer discriminant nodes, lowering the number of class dimensions to be evaluated progressively. The generated prediction models have been discovered to be expert short-running models with high forecast precision and low memory consumption with superior performance.
Furthermore, it was discovered that generated models might be classified as realistic decision support tools in several sectors of the construction business when compared to other accessible models. The suggested models may be an integrated, general, practical, and accurate prediction tool. The current study covers several significant traits employed in bidding and awarding procedures to reduce financial and legal concerns among contractual parties. As a result, the proposed models are expected to play a critical role in reducing potential conflict among stakeholders in the green building construction industry, particularly when decision-makers face significant challenges and difficulties in estimating acceptable green building costs that all contractual parties can agree on.
6. Conclusions
Will a green building cost more than a traditional building? Are the costs of the people, technological, technical, and other specific requirements quantifiable and predictable? Is this objective cost function affected by sub-attributes? Do the developed prediction models consider ambiguity in cost function? Do the developed cost forecasting models empower practitioners to take effective cost-related decisions? These research questions can be addressed by developing accurate and robust machine learning-based models for cost prediction to reduce the cost-related risk. The proposed models have been demonstrated to provide decision-makers with a support decision tool to forecast the green buildings’ construction costs of new green buildings and pave the road towards having green buildings LEED-certified based on economic and sustainable aspects. Four primary green building cost attributes and twenty sub-features were considered, and different feasible green construction approaches were investigated utilizing thorough cutting-edge forecasting models for cost prediction and associated risk minimization. ML-based cost prediction modeling approaches were utilized to improve decision-making superiority amongst the best practices. , , and prediction models were designed, and they were evaluated using , , , and . The evaluation results indicate that the and prediction performance was superior, where low values for all performance appraisal measures were evaluated, indicating excellent performance. The had a lower forecast accuracy, but it still had an acceptable level of precision. The most accurate green building costs can be predicted based on the embraced machine learning models.
The current study revealed that green building costs could be accurately predicted via machine learning approaches and smoothly compared with conventional building costs. In addition, the key attributes that influence green building costs were considered. Moreover, the developed cost prediction models are expected to pave the road toward a smoother certification process. Additionally, decision-makers are provided with support decision tools that can predict green buildings’ total operational and life cycle costs. Future research efforts should reflect the inclusion of more datasets, more accurate collection, pre-processing and post-processing, and different types of buildings in various locations. The external support attribute needs to be considered, as it is expected to significantly influence green building costs. Additionally, in future work, the economic assessment of the certification can be expanded beyond construction costs to incorporate the influence on the overall life cycle cost analysis.