Design errors have always been recognized as one of the main factors affecting safety and health management and sustainable development in surface mines. Unfortunately, scant attention is paid to design errors and the factors causing them. Therefore, based on expert opinions, this study aimed to identify, rank, and investigate cause-and-effect relationships among variables influencing human error in surface mine design in Iran. The study variables were identified by reviewing previous literature on “latent human errors” and “design errors.” After specifying effective variables, two rounds of the Fuzzy Delphi study were carried out to reach a consensus among experts. Nineteen variables with an influencing score of 0.7 and higher were screened and given to the experts to be analyzed for cause-and-effect relationships by the fuzzy DEMATEL method. The results of the study revealed that the following variables were the major factors affecting human error as root causes: poor organizational management (0.62), resource allocation (0.30), training level (0.27), and experience (0.25). Moreover, self-confidence (−0.29), fatigue (−0.28), depression (−0.25), and motive (−0.23) were found to be effect (dependent) variables. Our findings can help organizations, particularly surface mines, to opt for effective strategies to control factors affecting design errors and consequently reduce workers’ errors, providing a good basis for achieving sustainable development.

Mining activities are usually associated with negative outcomes. Therefore, it is crucial to identify and assess these outcomes by the mining company to achieve proper management. The present study has been defined to discover the outcomes of mining activities and their testing in one of the open pit mines of Iran. The present research has been defined into two sections, qualitative and quantitative. The corresponding data of the qualitative section were derived through analysis of the hidden contents of semi-structured interviews with experts and a review of the literature using the Maxqda 2022 software in the forms of open coding and axial coding. In the quantitative section of the study, data were collected via the standard questionnaire and analyzed using the SPSS26 and Mplus software. By coding the interviews and existing documents, 62 primary codes were extracted and classified into 5 main criteria (environmental, health, social, economic, and cultural) in the form of axial coding. The analysis results of the collected questionnaires showed that mining activities had the highest impact on the environment (86.32) and individual health (80.86), while the lower impact was on their economic situation (54.55). The findings of this study showed that there is a significant difference between men and women in terms of the environmental (p = 0.013) and economic (p = 0.01) indicators. While men believed that the mining activity had caused permanent environmental impacts on their living area, women recognized the mining activities as the cause of economic weakness in their families. Results from the present study could be effective in formulating the controlling strategies for potential negative outcomes of mining and achieving effective sustainable development.

BACKGROUND: Mines are often home to many dangers with a high rate of accidents and occupational diseases. One of the most effective ways to prevent these adverse incidents is to identify and control the influential factors causing human error in design and the ensuing negative consequences. OBJECTIVE: This study aimed to explore, categorize and prioritize factors affecting human errors in the mine design process. METHODS: The study has a mixed-method design combining qualitative and quantitative data. In the qualitative phase, the required data were collected by conducting semi-structured interviews with 12 surface mine designers. The causes of errors were extracted and categorized by the latent content analysis using MAXQDA2022 software. The identified causes in the qualitative phase were sent to expert designers in Q tables, and the data were analyzed by factor analysis. RESULTS: Of the identified codes in the qualitative phase, 40 main themes in five different categories (individual, organizational, external, task, and environmental factors) were determined as causes. The results of the quantitative phase suggest the existence of four different mental patterns regarding the causes of design errors (DEs). The data analysis also shows that organizational and personal factors, particularly supervision and inspection, experience, and technical knowledge, were the strongest causes of DEs and environmental (hotness, coldness, indoor air quality, and noise) and external (work-family conflict) factors being the weakest ones. CONCLUSION: This study not only identifies and categorizes the causes of design errors in the mining industry but also suggests some control strategies for these errors based on the mental patterns of the experts.

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