Subjective visual perception prediction of green construction sites based on TrueSkill ranking and deep learning

doi:10.11996/JG.j.2095-302X.2026030641

Abstract

Abstract:

Sustainable development serves as a core approach to addressing global environmental and energy challenges, while the construction industry has gradually implemented green construction practices, but the inherent connection between construction environment characteristics and human subjective perception has not yet been systematically explored. This gap results in a lack of targeted theoretical support for the optimization of green construction in terms of safety, aesthetics, and efficiency. To clarify the laws governing human perception of construction environments and thereby guide the optimization of green construction practices, this research collected preference data from participants regarding randomly matched combinations of two typical construction scenario images through an online crowdsourcing platform. The Microsoft TrueSkill system was employed to quantitatively rank the perceptual preference results. For the key visual elements in the construction scenarios, the Region of Interest (ROI)-based analysis method was used for feature extraction and interpretation. Meanwhile, several Convolutional Neural Networks (CNN) were selected and trained to enable the automated prediction of perceptual quality. The results indicate that there is a significant statistical correlation between the subjective perception of construction sites in the perceptual dimensions mentioned above and specific visual features: the stacking neatness of on-site building materials, ground cleanliness and equipment usage affect the perception about safety, aesthetics and efficiency, respectively. Due to the fact that the first two types of visual features are both external manifestations of the construction site orderiness, the perception of safety and aesthetics exhibits a strong positive correlation, while no correlation was observed between efficiency perception and the other two types of perception. An innovative construction environment evaluation framework was proposed, integrating crowdsourcing technology with deep learning. The consistency of visual perception of construction scenarios across different groups was clarified, and the specific visual standards required for civilized construction sites were clarified, providing a benchmark foundation for the establishment of an automated evaluation system for the perceptual quality of construction environments.

Key words: green construction, perceptual evaluation, deep learning

CLC Number:

LU Dehui, SONG Zhuo, HUANG Zhichao, TIAN Shiyu, LI Huimin, TIAN Mao, DENG Yichuan. Subjective visual perception prediction of green construction sites based on TrueSkill ranking and deep learning[J]. Journal of Graphics, 2026, 47(3): 641-652.

Figures/Tables 12

Fig. 1 Schematic diagram of technical route

Table 1 Perception indicator list

感知指标	目的
安全 (Safety)	测量观察施工现场时的安全感程度
美观 (Beauty)	测量施工现场的美观程度
效率 (Efficiency)	测量现场工人合作效率的感受程度

Fig. 2 Online questionnaire website

Fig. 3 Gaussian distribution model in TrueSkill algorithm

Fig. 4 Convolutional neural network architecture

Fig. 5 TrueSkill convergence process

Fig. 6 Example image of TrueSkill ranking results ((a) Safety; (b) Aesthetics; (c) Efficiency)

Fig. 7 Q-Score correlation test results based on demographic characteristics ((a) Men and women; (b) Around the median age; (c) Construction industry vs. other industries)

Fig. 8 ROI data example ((a) Safety; (b) Aesthetics; (c) Efficiency)

Table 2 CNN-based prediction accuracy on perception indicators

感知维度	AlexNet	VGG16	GoogleNet	LeNet
安全性	0.65	0.68	0.66	0.63
美观性	0.63	0.70	0.66	0.62
效率性	0.69	0.63	0.64	0.60

Table 3 Cross-prediction results in perception quality

CNN架构	安全性	美观性	效率性
VGG16 (安全性子集训练)	0.68	0.62	0.49
VGG16 (美观性子集训练)	0.63	0.70	0.51
AlexNet (效率子集训练)	0.52	0.51	0.69

Fig. 9 Cross-perceptual dimension Q-Score scatter plot analysis ((a) Safety and aesthetics; (b) Safety and efficiency; (c) Aesthetics and efficiency)

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