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Mega construction projects (megaprojects) require technological innovation cooperation (TIC) to address complex construction demands and the interests of multiple stakeholders. Although TIC has been extensively discussed at the firm level, a significant gap remains in understanding megaprojects at the project level. This paper aims to identify TIC’s influencing factors and transmission paths and discuss stakeholders’ TIC mechanisms at the project level.

Based on case analysis, expert interviews, literature analysis and the Delphi method, this paper identifies the influencing factors of TIC in megaprojects at the project level. A structural system of these influencing factors is constructed by interpretive structural modeling (ISM), developing various mechanisms for TIC from bottom to top. The Matriced’ Impacts Croisés Multiplication Appliquée à un Classement (MICMAC) method validates the driving forces and dependencies of the influencing factors, clarifying their roles and positions within the system. Additionally, the TIC mechanism is constructed.

The research findings identify 26 influencing factors categorized into four hierarchical levels: cooperative relationships, cooperative behavior, cooperative performance and technological innovation risks. Regarding direct factors, resource sharing affects goal congruence and communication effectiveness in megaprojects, affecting TIC’s satisfaction and trust. Most factors exist in the middle layer, and bridging the upper and lower levels depends on stakeholder collaboration. The root factors in the independent group significantly impact TIC, including policy circumstances, high technical requirements and limited site conditions. Addressing these issues influences improvements in other factors. The development of a digital resource-sharing platform, the enhancement of innovation incentives, the optimization of benefit distribution mechanisms and the improvement of risk-sharing mechanisms are essential for the effective operation of the TIC mechanism.

This study contributes to identifying and classifying challenges and opportunities in TIC. It explores transmission paths for enhancing TIC and presents strategies for successfully implementing and delivering megaprojects.

The purpose of this study is to develop a new hybrid method that combines interpretative structural modeling (ISM) and matrix cross-impact multiplication applied to classification (MICMAC) to investigate the influencing factors of sustainable infrastructure vulnerability (SIV).

(1) Literature review and case study were used to identify the possible influencing factors; (2) a semi-structured interview was conducted to identify representative factors and the interrelationships among influencing factors; (3) ISM was adopted to identify the hierarchical structure of factors; (4) MICMAC was used to analyze the driving power (DRP) and dependence power (DEP) of each factor and (5) Semi-structured interview was used to propose strategies for overcoming SIV.

Results indicate that (1) 18 representative factors related to SIV were identified; (2) the relationship between these factors was divided into a five-layer hierarchical structure. The 18 representative factors were divided into driving factors, dependent factors, linkage factors and independent factors and (3) 12 strategies were presented to address the negative effects of these factors.

The findings illustrate the factors influencing SIV and their hierarchical structures, which can benefit the stakeholders and practitioners of an infrastructure project by encouraging them to take effective countermeasures to deal with related SIVs.

An effective machine vision-based method for micro-crack detection of solar cell can economically improve the qualified rate of solar cells. However, how to extract features which have strong generalization and data representation ability at the same time is still an open problem for machine vision-based methods.

A micro-crack detection method based on adaptive deep features and visual saliency is proposed in this paper. The proposed method can adaptively extract deep features from the input image without any supervised training. Furthermore, considering the fact that micro-cracks can obviously attract visual attention when people look at the solar cell’s surface, the visual saliency is also introduced for the micro-crack detection.

Comprehensive evaluations are implemented on two existing data sets, where subjective experimental results show that most of the micro-cracks can be detected, and the objective experimental results show that the method proposed in this study has better performance in detecting precision.

First, an adaptive deep features extraction scheme without any supervised training is proposed for micro-crack detection. Second, the visual saliency is introduced for micro-crack detection.

This paper aims to improve the generalization capability of feature extraction scheme by introducing a micro-cracks detection method based on self-learning features. Micro-cracks detection of multicrystalline solar cell surface based on machine vision is fast, economical, intelligent and easier for on-line detection. However, the generalization capability of feature extraction scheme adopted by existed methods is limited, which has become an obstacle for further improving the detection accuracy.

A novel micro-cracks detection method based on self-learning features and low-rank matrix recovery is proposed in this paper. First, the input image is preprocessed to suppress the noises and remove the busbars and fingers. Second, a self-learning feature extraction scheme in which the feature extraction templates are changed along with the input image is introduced. Third, the low-rank matrix recovery is applied to the decomposition of self-learning feature matrix for obtaining the preliminary detection result. Fourth, the preliminary detection result is optimized by incorporating the superpixel segmentation. Finally, the optimized result is further fine-tuned by morphological postprocessing.

Comprehensive evaluations are implemented on a data set which includes 120 testing images and corresponding human-annotated ground truth. Specifically, subjective evaluations show that the shape of detected micro-cracks is similar to the ground truth, and objective evaluations demonstrate that the proposed method has a high detection accuracy.

First, a self-learning feature extraction method which has good generalization capability is proposed. Second, the low-rank matrix recovery is combined with superpixel segmentation for locating the defective regions.