Under the new wave of scientific and technological revolution, the construction industry finds itself with a critical need to alter the traditional and outdated production mode through technological innovation in order to realize industry transformation and move towards a new era characterized by digitalization, informatization, and intelligence. As intelligent construction is the indispensable pathway for the transformation and upgrading of the construction industry, it is of great significance to conduct in-depth research on its evaluation indicators and causality. This paper adopts the system dynamics method, based on the overall structure of intelligent construction, extracts the causality chain and causal feedback loop of intelligent construction, and presents a causality diagram and system dynamics diagram to build a robust system dynamics model for intelligent construction. On this basis, an evaluation index system for intelligent construction is constructed from the five dimensions—investment, design, construction, operation, and environment—for a holistic assessment of the current state of intelligent construction. The research aims to provide a valuable reference for professionals focusing on intelligent construction and the broader development of the industry.

Farmers’ attitude plays a crucial role in the success of land acquisition. To add new insights and theoretical support for the effective governance of land acquisition, this paper examines the effectiveness of administrative power on farmers’ attitude and the mediating role of perceived fairness based on asymmetric dependency theory. The hypotheses were verified using Structural Equation Modeling (SEM) analysis conducted with data gathered from 250 questionnaire samples in rural areas of China. The results reveal that the coercion and transparency of administrative power have a significant positive impact on farmers’ attitude while normality’s influence is not significant. The coercion and transparency of administrative power have a significant positive impact on fairness while normality’s impact is not significant. The mediating effect of perceived fairness was insignificant. Therefore, local governments could encourage farmers to embrace a cooperative attitude in land acquisition negotiations by fostering a constructive interaction between the government and the public and pursuing a high-level institutional balance driven by a service-oriented government.

This study proposes a quality-aware multimodal framework for health-state assessment and remaining useful life (RUL) prediction of cold-region tunnels. The framework integrates structural-response, environmental, apparent-defect, and engineering-inspectiondata, with the apparent-defect pathway jointly encoding raw images through a convolutional neural network and structured defect variables. Five data-quality dimensions-completeness, accuracy, consistency, timeliness, and traceability are incorporated intoreliability-guided multimodal fusion. Their base weights were re-audited through two rounds of expert consultation, each comprising 323 valid questionnaires. The Cr-weighted group analytic hierarchy process yielded weights of 0.0548, 0.1326, 0.1372, 0.2279, and 0.4474, respectively, with a group consistency ratio of 0.0455; the ranking remained stable under one-at-a-time +10% perturbations. In the primary tunnel case study, the framework achieved 89.7% health-state accuracy, a 6.3% RUL mean absolute percentage error, and 84.1% accuracy under Gaussian perturbation of standardized numerical inputs at a noise scale of 0.15. To further examine the reliability contribution of data-quality information, an independent field panel comprising 600 segment-month observations from 25 segments across three operational tunnels was evaluated using target-excluded specifications, two-way fixed effects, leave-one-tunnel-out validation, multiple baseline models, and five fixed random seeds. A one-standard-deviation increase in lagged quality instability was associated with a 0.0151 increase in the subsequent state-error index (95% CI: 0.0118-0.0184; p < 0.001). In cross-tunnel random-forest tests, incorporating quality information increased mean R² from 0.8277 to 0.8323 for state-error prediction and from 0.8517 to 0.8673 for RUL-contraction prediction, with both improvements significant in paired tests (p < 0.001). Split-conformal intervals achieved mean cross-tunnel coverage of 95.8% and 95.9%, respectively. These findings demonstrate that data-quality information provides a modest but statistically supported improvement in cross-tunnel reliability, whilethe principal contribution lies in integrating auditable data governance, reliability-aware fusion, and engineering decision support within a unified tunnel health-management framework.

With the growing popularity of ice sports, indoor ice sports venues are drawing an increasing number of spectators. Maintaining comfort in spectator zones presents a significant challenge for the operational scheduling of climate control systems, which integrate ventilation, heating, and dehumidification functions. To explore economic cost potential while ensuring user comfort, this study proposes a demand response-integrated optimization model for climate control systems. To enhance the model’s practicality and decision-making efficiency, a two-stage optimization method combining multi-objective optimization algorithms with the technique for order preference by similarity to an ideal solution (TOPSIS) is proposed. In terms of algorithm comparison, the performance of three typical multi-objective optimization algorithms—NSGA-II, standard MOEA/D, and Multi-Objective Brown Bear Optimization (MOBBO)—is systematically evaluated. The results show that NSGA-II demonstrates the best overall performance based on evaluation metrics including runtime, HV, and IGD. Simulations conducted in China’s cold regions show that, under comparable comfort levels, schedules incorporating dynamic tariffs are significantly more economically efficient than those that do not. They reduce operating costs by 25.3%, 24.4%, and 18.7% on typical summer, transitional, and winter days, respectively. Compared to single-objective optimization approaches that focus solely on either comfort enhancement or cost reduction, the proposed multi-objective model achieves a better balance between user comfort and economic performance. This study not only provides an efficient and sustainable solution for climate control scheduling in energy-intensive buildings such as ice sports venues but also offers a valuable methodological reference for energy management and optimization in similar settings.