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The swift integration of advanced technologies in the construction sector has significantly propelled the adoption of smart construction sites (SCSs). Quality management (QM), a critical endeavor within the construction domain, is central to the operational success of construction projects. The establishment of quality management in smart construction sites (SCS-QM) specifically seeks to delineate the principal factors influencing quality management in the context of SCS, with the objective of enhancing overall project quality. This study has identified 19 pivotal factors impacting SCS-QM by drawing upon the 4M1E quality management framework and an extensive review of the literature. Utilizing the hybrid DEMATEL-ISM-MICMAC analytical framework, the research evaluates these factors in terms of significance, hierarchical structure, and interdependencies, thereby formulating targeted strategies for the advancement of SCS-QM. Through a systematic evaluation by nine experts, this study categorizes the influencing factors into nine levels, three layers, and four areas, further classifying them into four distinct impact typologies. The results underscore that those technologies, such as automation and intelligence, along with regulatory frameworks, comprehensive quality management standards, transparency of critical technologies, training of construction personnel, and effective process management, constitute the foundational elements crucial for enhancing SCS-QM. [ABSTRACT FROM AUTHOR]

The burgeoning application of advanced information technology in the construction industry has led to the widespread adoption of smart construction sites (SCSs) in recent years. As a novel concept, smart construction site application maturity (SCS-AM) aims to identify the pivotal factors impeding the current progression of SCSs and foster the metamorphosis of the construction sector. Through a meticulous review of the existing literature, this study delineates 14 fundamental factors influencing SCS-AM. Employing both Exploratory Factor Analysis (EFA) and confirmatory factor analysis (CFA), alongside the acquisition of 217 valid questionnaires, practitioners' perceptions regarding these factors within the smart construction domain were examined. This study initially categorized the 14 factors into four dimensions by utilizing the EFA method: technological innovation and integration (TII), project management and implementation (PMI), collaboration mechanism and information sharing (CMIS), and standardization and compliance (SC). Subsequently, a first-order CFA was employed to elucidate the correlations between the observed variables and latent factors, while a second-order CFA was employed to delve into the interplay among the first-order factors and their collective influence on SCS-AM. The results underscore the paramount impact of standardization and compliance (SC) and technological innovation and integration (TII) on SCS-AM. By meticulously analyzing the key influencing factors, this study offers theoretical underpinnings for bolstering SCS-AM, thereby providing stakeholders such as governments and construction enterprises with strategic insights for future development endeavors. [ABSTRACT FROM AUTHOR]

The accurate identification of target imagery in the presence of sea fog is essential for the precise detection and comprehension of targets situated at sea. To overcome the issues encountered when applying traditional polarimetric dehazing methods to sea fog imagery, this paper proposes an improved polarimetric dehazing method. Initially, the methodology employs quartile-based selection on polarization difference images to ascertain atmospheric light at an infinite distance. Subsequently, the study describes a segmentation approach for sea–sky background images based on the degree of polarization. The results show that the image information entropy of the segmentation process improves by more than 6% compared to that of alternative methodologies, and the local contrast of the image is increased by more than 30% compared to that of the original foggy image. These outcomes confirm the effectiveness of the proposed dehazing methodology in addressing the challenges associated with sea fog imagery. [ABSTRACT FROM AUTHOR]