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"Highway construction"
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Application of stacking ensemble machine learning algorithm in predicting the cost of highway construction projects
PurposeThe purpose of this study to apply stacking ensemble machine learning algorithm for predicting the cost of highway construction projects.Design/methodology/approachThe proposed stacking ensemble model was developed by combining three distinct base predictive models automatically and optimally: linear regression, support vector machine and artificial neural network models using gradient boosting algorithm as meta-regressor.FindingsThe findings reveal that the proposed model predicted the final project cost with a very small prediction error value. This implies that the difference between predicted and actual cost was quite small. A comparison of the results of the models revealed that in all performance metrics, the stacking ensemble model outperforms the sole ones. The stacking ensemble cost model produces 86.8, 87.8 and 5.6 percent more accurate results than linear regression, vector machine support, and neural network models, respectively, based on the root mean square error values.Research limitations/implicationsThe study shows how stacking ensemble machine learning algorithm applies to predict the cost of construction projects. The estimators or practitioners can use the new model as an effectual and reliable tool for predicting the cost of Ethiopian highway construction projects at the preliminary stage.Originality/valueThe study provides insight into the machine learning algorithm application in forecasting the cost of future highway construction projects in Ethiopia.
Journal Article
Prediction Liquidated Damages via Ensemble Machine Learning Model: Towards Sustainable Highway Construction Projects
Highway construction projects are important for financial and social development in the United States. Such types of construction are usually accompanied by construction delay, causing liquidated damages (LDs) as a contractual provision are vital in construction agreements. Accurate quantification of LDs is essential for contract parties to avoid legal disputes and unfair provisions due to the lack of appropriate documentation. This paper effort sought to develop an ensemble machine learning technique (EMLT) that combines algorithms of the Extreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), k-Nearest Neighbor (kNN), Light Gradient Boosting Machine (LightGBM), Artificial Neural Network (ANN), and Decision Tree (DT) for the prediction of LDs in highway construction projects. Key attributes are identified and examined to predict the interrelated correlations among the influential features to develop accurate forecast models to assess the impact of each delay factor. Various machine-learning-based models were developed, where the different modeling outputs were analyzed and compared. Four performance matrices such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Coefficient of Determination (R2) were used to assess and evaluate the accuracy of the implemented machine learning (ML) algorithms. The prediction outputs implied that the developed EMLT model has shown better performance compared to other ML-based models, where it has the highest accuracy of 0.997, compared to the DT, kNN, CatBoost, XGBoost, LightGBM, and ANN with an accuracy of 0.989, 0.988, 0.986, 0.975, 0.873, and 0.689, respectively. Thus, the findings of this research designate that the EMLT model can be used as an effective administrative decision adding tool for forecasting the LDs. As a result, this paper emphasizes ML’s potential to aid in the advancement of computerization as a comprehensible subject of investigation within highway building projects.
Journal Article
Analysis of the Drivers of Highway Construction Companies Adopting Smart Construction Technology
In this study, we aimed to identify the influencing factors that drive highway construction companies to adopt smart construction technologies. Using expert interviews and expert scoring, we collected interview data from 25 experts in the field and we proposed the TOSE framework based on the TOE framework, identifying four dimensions and fourteen influencing factors. We analyzed the results using the Fuzzy DEMATEL-ISM method, and we then summarized the findings according to the evaluation criteria to determine the validity of the fourteen hypotheses and the extent to which they drive highway construction companies to adopt smart construction technologies. The findings of this paper are of high value to decision makers and participants in highway construction companies, as well as to other companies in the construction industry, in their decision to adopt smart construction technologies.
Journal Article
Management of environmental risks in highway construction projects in Sri Lanka
Purpose
Environmental risks (ERs) are critical to any highway construction project (HCP). One of the main contracting parties responsible for ERs is the contractor. Hence, it has been crucial to look into ways to control ERs in HCPs from the contractor’s perspective. This study aims to investigate how ERs can be managed in HCP in Sri Lanka.
Design/methodology/approach
A quantitative research approach with three rounds of Delphi was used. Statistical techniques were used to analyse and validate the ERs, the parties to whom the risks were to be allocated, and risk management measures identified from the empirical data collection.
Findings
The study reveals the 11 most significant ERs for HCP. Further, the most significant ERs in HCP were mainly found to be the responsibility of contractors in Sri Lanka. Twenty-four most appropriate risk response measures were determined; 13 were found to be common measures that could be used to manage two or more risks, while the remaining 11 were unique to specific risks.
Originality/value
Overall, this research determines the most significant ERs in HCP, the best risk allocation among the parties and appropriate risk-handling strategies and measures for each significant ERs. Additionally, the study addresses the demand for ERs management in HCP.
Journal Article
Sustainability Indicator Selection by a Novel Triangular Intuitionistic Fuzzy Decision-Making Approach in Highway Construction Projects
The construction industry has been criticized as being a non-sustainable industry that requires effective tools to monitor and improve its sustainability performance. The multiplicity of indicators of the three pillars of sustainability—economic, social, and environmental—complicates construction sustainability assessments for project managers. Therefore, prioritizing and selecting appropriate sustainability indicators (SIs) is essential prior to conducting a construction sustainability assessment. The main purpose of this research is to select the most appropriate set of SIs to address all three pillars of highway sustainability by a new group decision-making approach. The proposed approach accounts for risk attitudes of experts and entropy measures under a triangular intuitionistic fuzzy (TIF) environment, to handle the inherent uncertainty and vagueness that is present throughout the evaluation process. Furthermore, new separation measures and ranking scores are introduced to distinguish the preference order of SIs. Eventually, the approach is implemented in a case study of highway construction projects and the applicability of the approach is examined. To investigate the stability and validity of computational results, a sensitivity analysis is carried out and a comparison is made between the obtained ranking outcomes and the traditional decision-making methods.
Journal Article
Cognitive-Inspired Multimodal Learning Framework for Hazard Identification in Highway Construction with BIM–GIS Integration
Highway construction remains one of the most hazardous sectors in the infrastructure domain, where persistent accident rates challenge the vision of sustainable and safe development. Traditional hazard identification methods rely on manual inspections that are often slow, error-prone, and unable to cope with complex and dynamic site conditions. To address these limitations, this study develops a cognitive-inspired multimodal learning framework integrated with BIM–GIS-enabled digital twins to advance intelligent hazard identification and digital management for highway construction safety. The framework introduces three key innovations: a biologically grounded attention mechanism that simulates inspector search behavior, an adaptive multimodal fusion strategy that integrates visual, textual, and sensor information, and a closed-loop digital twin platform that synchronizes physical and virtual environments in real time. The system was validated across five highway construction projects over an 18-month period. Results show that the framework achieved a hazard detection accuracy of 91.7% with an average response time of 147 ms. Compared with conventional computer vision methods, accuracy improved by 18.2%, while gains over commercial safety systems reached 24.8%. Field deployment demonstrated a 34% reduction in accidents and a 42% increase in inspection efficiency, delivering a positive return on investment within 8.7 months. By linking predictive safety analytics with BIM–GIS semantics and site telemetry, the framework enhances construction safety, reduces delays and rework, and supports more resource-efficient, low-disruption project delivery, highlighting its potential as a sustainable pathway toward zero-accident highway construction.
Journal Article
Strategies of Metaverse Safety Training in Highway Construction Projects: A Tripartite Evolutionary Game
Metaverse safety training (MST) is popular in highway construction projects (HCPs). While researchers have statically examined the influence of MST, one of the essential gaps is that the interaction among stakeholders on how to improve MST effect is neglected. This paper adopts a game theory approach to illustrate the dynamics among stakeholders, namely, contractors, subcontractors, and construction crews, regarding MST within the framework of HCPs. A tripartite evolutionary game model is developed to analyze the interaction among contractors, subcontractors, and construction crews. The evolutionary stability of the stakeholders’ strategies and the equilibrium point were elucidated by solving the proposed model. A numerical simulation was conducted to validate the rationality of the results. The results show that the choice of behavioral strategies and their evolutionary paths for each stakeholder are closely related to the behavioral strategies of other stakeholders in the game, with significant differences in effects on each other’s initial strategies. The incentive mechanism must match the incentive measures provided to subcontractors and construction crews, ensuring a stable MST. The reward and penalty system implemented by contractors heightens the awareness of subcontractors and construction crews partly. This model provides practical recommendations to enhance training interactions, optimize strategies, increase security awareness, and streamline resource allocation.
Journal Article
A Comparison of Construction Accident Cognition: A Case Study of Construction Managers
Construction safety performance relies on the complex management decision-making process, directly impacting safety results. In this study, the management-level staff working on highway construction projects in Taiwan were surveyed on the extent to which they identified unsafe behaviours of construction workers. In the study, a total of 191 valid responses were collected through a structured questionnaire from highway construction site managers in Taiwan. Quantitative methods, including chi-square tests, ANOVA, and Pearson correlation analysis, were used to test four hypotheses regarding the influence of personal and site-related factors on the accident cognition of manager-level staff. Leadership qualities, together with work experience and educational attainment, were found to determine how managers identify and address safety risks. The strong relationship between site conditions and accident cognition validated all four research hypotheses posed. Furthermore, effective management was found to play an essential part in developing a safety culture, which leads to decreased construction accidents. The experience, education, and safety education and training of the highway construction site managers are significantly correlated with accident perception. Individual variables such as the degree of identification, institutional aspects, and implementation aspects are the main factors that trigger unsafe behaviours of construction workers. The construction managers' site safety management and background experience can be used to predict workers' safety attitudes, safe habits and occupational accidents.
Journal Article