Granite is a well-known building and decorative material, and, therefore, the amount of produced waste in the form of granite powder is a problem. Granite powder affects the health of people living near landfills. Dust particles floating in the air, which are blown by gusts of wind, can lead to lung silicosis and eye infections, and can also affect the immune system. To find an application for this kind of waste material, it was decided to study the effect of partially replacing cement with waste granite powder on the properties of fresh and hardened mortars intended for masonry applications. The authors planned to replace 5%, 10%, and 15% of cement with waste material. Series of mortar with the addition of granite powder achieved 50% to 70% of the compressive strength of the reference series, and 60% to 76% of the bending strength of the reference series. The partial replacement of cement with the granite powder significantly increased the water sorption coefficient. The consistency of the fresh mortar, and its density and water absorption also increased when compared to the reference series. Therefore, Granite powder can be used as a partial replacement of cement in masonry mortars.

The construction sector is notorious for its high rate of fatalities globally. Previous research has established that near-miss incidents act as precursors to accidents. This study aims to identify research gaps in the literature on near-miss events in construction and to define potential directions for future research. The Scopus database serves as the knowledge source for this study. To identify publications on near-miss events, the search field “Article Title, Abstract, Keywords” was utilized with the keywords “construction” and “near miss”. The main research themes were defined based on keyword mapping performed using VOSviewer. Selected publications were assessed for their alignment with the defined research theme. A statistical analysis of the publications and the co-occurrence of keywords was conducted. The authors of the identified publications primarily used statistical analyses, artificial intelligence, employee monitoring, tracking systems, and building information modelling in their research. The conclusions from the literature review indicate a need for further research focused on developing effective predictive models for workplace accidents based on knowledge of near-miss events. This will contribute to a better understanding of the mechanisms leading to accidents and their prevention, ultimately resulting in a significant reduction in accidents in the construction sector.

This article proposes a methodology for classifying occupational accidents involving scaffolding based on the knowledge of the causes that led to their occurrence. Each occupational accident is caused by several causes belonging to three generic groups (technical, organizational, human) occurring in a different configuration. The aim of this research was to determine the qualitative and quantitative structure of the causes of accidents caused by falling from scaffolding. Significant causes were selected from the set of all the causes identified in the analyzed set of accidents. For this purpose, Pareto–Lorenz analysis and the ABC classification were used. Then, a set of significant causes containing technical, organizational and human causes was created, which was the basis for determining the subsets of accidents caused by similar causes. The hierarchical cluster analysis method, the agglomeration clustering technique and the binding of objects using the Ward method were proposed to determine the number of characteristic clusters. Three subsets of accidents with a similar set of causes were obtained. Information on the quality and number of causes in individual subsets was used to estimate the probability of an accident caused by a given set of causes and to assess occupational risk in construction. Calculations were performed using Statistica software.

Near misses are referred to in literature as precursors of occupational accidents. These events, and their relationships with occupational accidents in various economic sectors have been the subject of research in several countries around the world. In Poland, there has not been a lot of research with regards to near misses in the construction industry, and the published materials are of a very general nature. This article aims to fill the existing research gap regarding the relationship between near misses and occupational accidents in the Polish construction industry. The aim of the research presented in the article was to identify the qualitative and quantitative structure of hazardous events in the construction industry, and to estimate the a posteriori probability of the occurrence of such events. The research was carried out on the basis of information obtained from the hazardous event registration system from a large Polish enterprise that carried out construction works in Poland in the years 2015-2022. Identified dangerous events in the construction industry were divided into two generic groups, i.e. occupational accidents and near misses. Within each group, eight categories of hazardous events were identified, and subcategories were defined within each category. The power of each set and subset of events was then calculated. The posterior probability of the occurrence of events classified into individual sets was estimated using Bayes' theorem. Cross-validation of the obtained results was performed. Conclusions resulting from the conducted analyzes were formulated. The most common category of the analyzed dangerous events in the construction industry is "being hit by objects"; "being run over/falling over" and "work environment". Knowledge of the most common causes of near misses will allow actions that aim to reduce the number of occupational accidents to be determined.

Despite preventive measures, the construction industry continues to exhibit high accident rates. In response, visual detection system was developed to support safety management on construction sites and promote sustainable working environments. The solution integrates the YOLOv8 algorithm with asynchronous video processing, incident registration, an open API, and a web-based interface. The system detects the absence of safety helmets (NHD) and worker falls (FD). Its low hardware requirements make it suitable for small and medium-sized construction enterprises, contributing to resource efficiency and digital transformation in line with sustainable development goals. This study advances practice by providing an integrated, low-resource solution that unites multi-hazard detection, event documentation, and system interoperability, addressing a key gap in existing research and implementations. The contribution includes an operational architecture proven to run in real time, addressing a gap between model-centred research and deployable, OHS applications. The system was validated using two independent test datasets, each comprising 100 images: one for NHD and one for FD. For NHD, the system achieved a precision of 0.93, an accuracy of 0.88, and an F1-score of 0.79. For FD, a precision of 1.00, though with a limited recall of 0.45. The results demonstrate the system’s potential for sustainable construction site safety monitoring.