Buildings· 2026Q1
A Novel Live Load Survey Approach for Classroom Buildings by Group Intelligence: Methodology and Application
- 0citations
- Q1SCImago
- 2026year
Short summary
A new survey method uses group intelligence to collect classroom live load data, aggregating volunteer-reported load samples and electronic inventory weights, which yielded a large dataset from 299 classrooms.
AI-generated from the title and abstract; the full text is not read.
Key points
- Proposes a group intelligence-based survey method for classroom live loads, combining volunteer-reported load samples and electronic inventory weights.
- Addresses limitations of traditional physical sampling and on-site weighing methods, which are labor-intensive and inefficient.
- Successfully applied the methodology across four provinces in China, collecting data from 299 classrooms covering 22,921 m2.
- The study includes field measurements to verify feasibility and presents statistical analysis and probabilistic modeling of the collected data.
AI-generated from the title and abstract; the full text is not read.
Abstract
Floor live load is a governing parameter for engineering structures, and its reliable quantification is critical during both the design phase and the structural service life. Although classrooms represent a common building category, research regarding their live load characteristics remains relatively limited. Existing studies generally rely on physical sampling and on-site weighing. These methods are labor-intensive and inefficient, which limits their applicability to large-scale surveys. To address these challenges, this paper proposes a novel survey approach for classroom live loads based on group intelligence technology. This method aggregates load amplitude samples by recruiting volunteers through questionnaires. Meanwhile, the weights of indoor items are determined via an electronic inventory method. Field measurements verified the feasibility of this approach. The methodology was subsequently applied across four provinces in China. This implementation yielded an extensive dataset comprising 299 classrooms and covering a total floor area of 22,921 m2. Finally, the study presents a statistical analysis and probabilistic modeling of the results.
The authors' abstract, as published at the source. Buildings, 2026 · DOI ↗
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Field: Civil and Structural Engineering
Civil and Structural EngineeringEngineering