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The negative effects of job stress and burnout on construction professionals (CPs) at the construction site have been widely concern in the construction industry. The purpose of this study is committed to explore the impact of job stress on CPs on the construction site, especially in the context of the widespread use of social media to express their emotions.

This study developed a job-related stress-burnout-health conditions-turnover intention (S-B-HT) framework to explore the direct and lagged effects of job stress, we also examined the moderating effects of online emotions, operationalized in terms of emotional intensity and expression pattern, on the relationship between job stress with job burnout under two evolution paths (i.e. health conditions or turnover intention). This study collected 271 samples through a survey questionnaire for empirical testing, and introduced structural equation models to validate the proposed conceptual model.

The results show that job stress has a significant positive effect on job burnout, and job burnout maintains a positive relationship with health conditions (or turnover intention) under the interference mechanism. Simultaneously, the online emotions expressed in social media have a positive moderating effect in two stages of the evolution path.

The findings of this study remind the project manager need to timely find and solve the job burnout characteristics of CPs due to excessive job stress, especially to prevent the accidental consequences caused by job burnout.

On this basis, this study provides an important value of using social media to express emotions for the project team to alleviate the adverse of professionals under job stress.

The objective of this study is to accurately predict the cost of green buildings to provide quantifiable criteria for investment decisions from investors.

This study proposes a hybrid prediction model ML-based for cost prediction of GBPs and obtains prediction parameters (PPs) associated with project characteristics through data mining (DM) techniques. The model integrates a principal component analysis (PCA) method to perform parameter dimensionality reduction (PDR) on a large number of raw variables to provide independent characteristic terms. Moreover, the support vector machine (SVM) algorithm is improved to optimize the prediction results and integrated with parameter dimensionality reduction and cost prediction.

The prediction results show that the mean absolute and relative errors of the hybrid prediction model proposed in this study are equal to 39.78 and 0.02, respectively, which are much lower than those of the traditional SVM model and MRA prediction model. Moreover, the hybrid prediction model with parameter dimensionality reduction also achieved better prediction accuracy (R² = 0.319) and superior prediction accuracy for different cost terms.

Theoretically, the hybrid prediction model developed in this study can reliably predict the cost while accurately capturing the characteristics of GBPs, which is a bold attempt at a comprehensive approach. Practically, this study provides developers with a new ML-based prediction model that is capable of capturing the costs of projects with ambiguous definitions and complex characteristics.

This paper addresses the issue of internal spatial environmental pollution in non-blasting tunnel construction by proposing a comprehensive evaluation model. The model aims to provide a scientific basis for environmental pollution prevention in non-blasting tunnel construction, thereby facilitating green tunnel construction and sustainable development management.

The study firstly refines and constructs the evaluation index system from the perspective of pollution sources. A novel weight calculation method is introduced by integrating the analytic hierarchy process (AHP) with the ordered weighted averaging (OWA) operator, and a comprehensive evaluation model for internal environmental pollution in non-blasting tunnels is established by incorporating the grey clustering evaluation method. Finally, an empirical study is conducted using the Erbaoshan Tunnel as a case study to verify the feasibility and effectiveness of the model.

The study develops an evaluation system for internal environmental pollution in non-blasting tunnels and applies it to the Erbaoshan Tunnel. The results classify the pollution level as “general pollution,” confirming the rationality and applicability of the evaluation system and model while also identifying the primary pollution factors.

This study first developed a comprehensive evaluation system for environmental pollution in non-blasting tunnel construction from the pollution source perspective, making the system more comprehensive. Additionally, it innovatively combined AHP–OWA and gray clustering methods to scientifically assess pollution levels, providing valuable scientific guidance for the evaluation and management of non-blasting tunnels and similar underground projects.

Continuum robots offer unique advantages in various specialized environments, particularly in confined or hard-to-reach spaces. Inverse kinematics and real-time shape estimation constitute crucial aspects of closed-loop control for continuum robots, presenting challenging problems. This paper aims to present an inverse kinematics and shape reconstruction method, which relies solely on the knowledge of base and end positions and orientations.

Based on the constant curvature assumption, continuum robots are regarded as spatial curves composed of circular arcs. Using geometric relationships, the mathematical relationships between the arc chords, points on the bisecting plane and the coordinate axes are established. On this basis, the analytical solution of the inverse kinematics of the continuum robots is derived. Using the positions and orientations of the base and end of the continuum robots, the Levenberg–Marquardt algorithm is used to solve the positions of the cubic Bezier curves, and a new method of spatial shape reconstruction of continuum robots is proposed.

The inverse kinematics and spatial shape reconstruction simulation of the continuum robot are carried out, and the spatial shape measurement experimental platform for the continuum robot is constructed to compare the measured and reconstructed spatial shapes. The results show that the maximum relative error between the actual shape and the reconstructed shape of the continuum robot is 2.08%, which verifies the inverse kinematics and shape reconstruction model. Additionally, when the bending angle of a single bending section of the continuum robot is less than 135°, the shape reconstruction accuracy is higher.

The proposed inverse kinematics solution method avoids iterative solving, and the shape reconstruction model does not rely on mechanical models. It has the advantages of being simple to solve, highly accurate and fast in computation, making it suitable for real-time control of continuum robots.