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Crack detection of pavement is a critical task in the periodic survey. Efficient, effective and consistent tracking of the road conditions by identifying and locating crack contributes to establishing an appropriate road maintenance and repair strategy from the promptly informed managers but still remaining a significant challenge. This research seeks to propose practical solutions for targeting the automatic crack detection from images with efficient productivity and cost-effectiveness, thereby improving the pavement performance.

This research applies a novel deep learning method named TransUnet for crack detection, which is structured based on Transformer, combined with convolutional neural networks as encoder by leveraging a global self-attention mechanism to better extract features for enhancing automatic identification. Afterward, the detected cracks are used to quantify morphological features from five indicators, such as length, mean width, maximum width, area and ratio. Those analyses can provide valuable information for engineers to assess the pavement condition with efficient productivity.

In the training process, the TransUnet is fed by a crack dataset generated by the data augmentation with a resolution of 224 × 224 pixels. Subsequently, a test set containing 80 new images is used for crack detection task based on the best selected TransUnet with a learning rate of 0.01 and a batch size of 1, achieving an accuracy of 0.8927, a precision of 0.8813, a recall of 0.8904, an F1-measure and dice of 0.8813, and a Mean Intersection over Union of 0.8082, respectively. Comparisons with several state-of-the-art methods indicate that the developed approach in this research outperforms with greater efficiency and higher reliability.

The developed approach combines TransUnet with an integrated quantification algorithm for crack detection and quantification, performing excellently in terms of comparisons and evaluation metrics, which can provide solutions with potentially serving as the basis for an automated, cost-effective pavement condition assessment scheme.

The paper aims to develop a useful tool that can reliably and accurately find the critical paths of high-rise buildings and provide optimal solutions considering the uncertainty based on Monte Carlo simulation (MCS) to enhance project implementation performance by assisting site workers and project managers in high-rise building engineering.

This research proposes an approach integrating the improved nondominated sorting genetic algorithm II (NSGA-II) considering uncertainty and delay scenarios simulated by MCS with the technique for order preference by similarity to an ideal solution.

The results demonstrate that the proposed approach is capable of generating optimal solutions, which can improve the construction performance of high-rise buildings and guide the implementation management for shortening building engineering project schedule and cost under the delay conditions.

In this study, only the construction data of the two floors was focused due to the project at the construction stage, and future work can analyze the whole construction stage of the high-rise building to examine the performance of the approach, and the multi-objective optimization (MOO) only considered two factors as objectives, where more objectives, such as schedule, cost and quality, can be expanded in future.

The approach proposed in this research can be successfully applied to the construction process of high-rise buildings, which can be a guidance basis for optimizing the performance of high-rise building construction.

The innovations and advantages derived from the proposed approach underline its capability to handle project construction scheduling optimization (CSO) problems with different performance objectives under uncertainty and delay conditions.

Owing to multiple superiorities to traditional counterparts, prefabricated construction (PC) has gained increasing attention worldwide. The development of PC projects reflects the effects of both policy supervision and PC practice, which aids the government in reasonably identifying the key issues of PC's promotion and rationally improving the policy deployment. However, existing studies fail to address this aspect, especially lacking quantitative exploration. This study explores the micro mechanism of PC's promotion, from the perspective of developing PC projects.

A tripartite evolutionary game model based on prospect theory of the government, developers and contractors is constructed. After rigorous theoretical deduction, this study adopts Changchun in China as a case city and collects the data using the Delphi technique, policy documents and literature analysis.

Results indicate that contractors are generally willing to implement PC projects and the government chooses to actively supervise PC's promotion. The negative investment behavior of developers is the main obstacle to promote PC in Changchun currently.

The conclusions are applicable to other comparable regions. This study is of value to promote PC with high efficiency and effect.

The tripartite evolutionary game model based on prospect theory proposed in this study is conducive to reveal the essence of PC's promotion. This is an important breakthrough in extant studies, with a broad applicability in the PC domain beyond China.

The purpose of this paper is to examine the effect of top management’s environmental responsibility audit (ERA) implementation on firms’ investment for environmental protection in China.

The sample comprises firms publicly traded on A-Share in China from 2011 to 2017. The authors used the ordinary least squares regression model to test the relation between ERA implementation and corporate environmental investment.

Firms’ environmental investment increases significantly after the ERA implementation. Compared to state-owned enterprises (SOEs), non-state-owned enterprises (non-SOEs) are more likely to increase their environmental investment after ERA implementation. Moreover, such change is more likely for non-heavily polluting enterprises (non-HPEs) compared to heavily polluting enterprises (HPEs).

This paper provides an in-depth analysis of the positive influence of environmental enforcement on corporate behavior, which could serve as reference for regulators on the latest environmental accounting practice in China and other emerging economies.

This paper shows that clear assignment of environmental responsibility and subsequent assessment of environmental performance are contributing factors to effective and efficient implementation of an environmental management system.

Contributing to accounting and environmental management literature, this paper explains how mandated environmental audit incentivizes firms to deal with environmental issues. Because there is no prior research concerning the mandatory implementation of environmental audit in China, this paper is of high-innovatory value by providing a better understanding of environmental auditing and providing an economic explanation for government intervention as an effective means of mitigating environmental degradation in emerging economies.