Bridges have a special place in transportation infrastructures and road networks due to their direct relationship with other places. These structures have the purpose of maintaining the traffic loads of the highway, crossing any obstacle, and performing effective communication between two destinations. Costs associated with bridge maintenance continue to be expensive due to their widespread use and stringent inspection requirements. Many researchers have been working on methods to use machine-learning (ML) techniques to forecast specific situations rather than physically checking bridges as part of the maintenance process in recent years. The practical value of the models has, however, been severely constrained by issues such relatively poor model evaluation results, unstable model performances, and the ambiguous application of established models in real-world scenarios. This work showed a thorough method of bridge condition prediction model building from feature engineering to model evaluation, along with a clear procedure of applying the produced model to actual usage, using data from the United States National Bridge Inventory (NBI) and the Adaboost algorithm. Multiple ML model assessment metrics’ findings revealed that the given model outperformed the majority of earlier studies in terms of values and stability. The case study demonstrated that there is a 30% reduction in the number of bridges that need to be inspected. This study serves as a crucial resource for the practical application of ML approaches in the forecast of the status of civil infrastructure. Additionally, it shows that boosted ML models may be a superior option as modeling algorithms advance. To explore the main influencing aspects of bridge conditions, a predictor importance analysis is also offered.

During the drastic changing process of the construction industry in China, construction program management has been given significant attention. Due to the complexity of construction programs, selecting competent managers is crucially important to its success. Therefore, based on a comprehensive literature review, this paper combines regret theory and the Fuzzy-DEMATEL method to develop a multi-attribute model for construction program manager selection. Firstly, six competence elements are extracted, then the manager selection and evaluation index system are constructed. Secondly, the regret theory is used to simulate the psychological characteristics of the decision makers, combined with Fuzzy-DEMATEL, the comprehensive weights for each element are calculated. Lastly, all alternatives for the selection are sorted and the competent ones are selected. A case study is provided to exam the effectiveness of the developed model. Results shows that the proposed model adopted multi-attribute evaluation and group decision making and took into account the psychological behavior of decision makers as well as influences from the relationships between different attributes. Such results indicate that the proposed model is able to provide more comprehensive and scientific construction program manager selections, which can further improve the management of construction programs.

The selection of suitable subcontractors for large construction companies is crucially important for the overall success of their projects. As the construction industry advances, a growing number of criteria need to be considered in the subcontractor selection process than simply considering the biding prices. This paper proposed a hybrid multi-criteria structure entropy weight (SEW)—TOPSIS group decision-making model that considers 10 criteria. The proposed model was able to handle large amount of subcontractors’ performance data that were collected in different types. Additionally, the model can integrate experts’ judgments while accounting for their varying level of expertise and correcting for their biases. This paper also provided a case study to demonstrate the proposed model’s effectiveness and efficiency, as well as its applicability of large construction companies. While this study was applied to construction subcontractors’ selection, the proposed methodology can also be easily extended to various decision-making scenarios with similar requirements.

For managers of road infrastructure, culvert deterioration is a major concern since culvert failures can cause serious risks to the traveling public. The efficiency of the cost‐ and labor‐intensive culvert inspection and maintenance process can be improved by properly identifying the key impact factors on culvert condition deterioration. Although the use of machine learning (ML) techniques to predict culvert conditions has been proven to be a promising tool for enhancing culvert management and enabling proactive scheduling of maintenance tasks, the information provided by the developed ML models has been given little attention for further use and analysis. By utilizing the predictor importance results of an evaluated decision tree (DT) culvert condition prediction model and the Mann–Whitney U test, this study provided insights to the identification of the key variables influencing culvert deterioration. According to the findings, five impact factors, including culvert span, pH, age, rise, and cover height, often have significant impact on the condition ratings of culverts made of various materials. In addition, such a statistical test‐assisted factor identification process offered a way of identifying and enhancing the input variable selection for predictive ML model development.

This study statistically evaluates the suitability of the construction industry as a diversification target for infrastructure corporations by integrating Data Envelopment Analysis (DEA-BCC), panel data regression, and entropy-based diversification metrics. Twenty actively operating highway companies in China were selected, and their annual reports from 2011 to 2021 were meticulously reviewed. This study quantifies the impact of diversification strategies on firm performance, resource allocation efficiency, and X-inefficiency. The findings reveal that strategic diversification into construction-related activities enhances operational efficiency and mitigates X-inefficiency, aligning with Penrose’s theory of surplus resource deployment. The study contributes a robust analytical framework for assessing diversification in the infrastructure sectors, offering actionable insights for corporations considering expansion into construction. The results underscore the importance of organizational optimization and business capability in reducing X-inefficiency and enhancing sustainable development, particularly in maturing markets where traditional growth avenues are limited.

In recent years, as the era of electronic payment has brought about the development of intelligent multi-scale forecasting of highway traffic in my country, it has also been developed because of its late start. Expressway intelligence, in simple terms, is the combination of human operation intelligent technology, digital transmission and analysis and professional skills mastery in the process of highway management. This technology can imitate people's thinking and analysis capabilities to ensure that the original plan is normal run. The core point of the goal of realizing highway intelligence is the relationship between the road, the driving vehicle and the owner and the establishment of a relatively stable and efficient transportation route. From the perspective of intelligent areas that have implemented multi-scale prediction of expressway traffic, it is mainly reflected in the form of ETC, multi-scale prediction of networked traffic, that is, according to the province or larger area, a one-time payment method is adopted for all-in-one cards. Form a complete set of intelligent flow multi-scale prediction system system, maximize the consideration of road users and economic development needs, effectively solve the closed flow multi-scale prediction system generated by different attributions or business entities and improve the efficiency of expressway use and service quality , Reduce the corresponding energy consumption and lay a good foundation for the sustainable development of highways in our country.