Understanding how university members conceptualize sexual harassment is crucial, as their perceptions influence recognition, reporting, and response to the issue. The aim of this study was to explore the discourses of key university informants about sexual harassment within universities, including student representatives, university staff, and institutional representatives in equality and diversity. A qualitative approach using semi-structured interviews (November 2023 to April 2024) with 90 key informants from six universities located in Austria, Belgium, Italy, Poland, Portugal, and Spain. Data were analyzed using Critical Discourse Analysis. Results show that sexual harassment is perceived as a multidimensional phenomenon encompassing physical, verbal, non-verbal, offline, and online behaviors. Participants’ accounts consistently emphasized the transgression of interpersonal boundaries, the absence of consent, power imbalances, and negative consequences on victims-survivors’ well-being. Participants’ definitions fell along a continuum of three discursive approaches— normative, experience-based, and integrative—which varied according to gender and institutional role. Regarding potential victim-survivors, two discourses emerged: an intersectional discourse frames vulnerability as the result of overlapping social inequalities—such as gender, age, hierarchical position, sexual orientation, ethnicity, migration background, disability, and/or economic dependence; and a situational discourse emphasizing risks associated with structural dynamics of academia, including hierarchy and dependency, regardless of individual characteristics. Concerning potential perpetrators, a hierarchical discourse was dominant among participants, alluding to men in positions of authority and leadership within the university. Two minoritarian and alternative discourses emerged: a peer perpetration discourse, situating harassment among individuals of similar hierarchical status, particularly students; and a psychological characteristics discourse, explaining perpetration in terms of individual traits, such as narcissism, manipulation, or egocentrism. Our findings suggest the need for university policies that integrate intersectional and gender-transformative perspectives and promote egalitarian and safer academic environments.

Cost estimation and as-built documentation on construction sites require extensive manual work. Combining LiDAR with computer vision offers an effective solution for 5D BIM applications. Therefore, two deep neural networks are trained on a database of over 1000 images of radiators, as example of MEP components. Transfer learning has been applied on SOLOv2 for instance segmentation and on YOLOx for object detection. Given the variety of the image database, the generated models achieve satisfying performance, suitable for the intended applications. The developed models will be used in a scan-to-BIM workflow, enabling the semi-automatic capture of as-built information.