Search Results (673)

The social acceptance of Renewable Energy Sources (RESs) is a decisive factor in the successful implementation of clean energy projects. This study explores the attitudes, demographic profiles, and common misconceptions of citizens in the Region of Epirus, Greece, toward photovoltaic and wind energy installations. Special attention is given to the role of education, age, and access to information—as well as spatial factors such as visual exposure—in shaping public perceptions and influencing acceptance of RES deployment. A structured questionnaire was administered to 320 participants across urban and rural areas, with subdivision between regions with and without visual exposure to RES infrastructure. Findings indicate that urban residents exhibit greater acceptance of RES, while rural inhabitants—especially those in proximity to installations—express skepticism, often grounded in esthetic concerns or perceived procedural injustice. Misinformation and lack of knowledge dominate in areas without visual contact. Statistical analysis confirms that younger and more educated participants are more supportive and environmentally aware. The study highlights the importance of targeted educational interventions, transparent consultation, and spatially sensitive communication strategies in fostering constructive engagement with renewable energy projects. The case of Epirus underscores the need for inclusive, place-based policies to bridge the social acceptance gap and support the national energy transition. Full article

The construction industry has been troubled by a shortage of skilled labor and safety accidents in recent years. Therefore, more and more robots are introduced to undertake dangerous and repetitive jobs, so that human workers can concentrate on higher-value and creative problem-solving tasks. Nevertheless, although human–robot collaboration (HRC) shows great potential, most existing evaluation methods still focus on the single performance of either the human or robot, and systematic indicators for a whole HRC team remain insufficient. To fill this research gap, the present study constructs a comprehensive evaluation framework for HRC team performance in construction projects. Firstly, a detailed literature review is carried out, and three theories are integrated to build 33 indicators preliminarily. Afterwards, an expert questionnaire survey (N = 15) is adopted to revise and verify the model empirically. The survey yielded a Cronbach’s alpha of 0.916, indicating excellent internal consistency. The indicators rated highest in importance were task completion time (µ = 4.53) and dynamic separation distance (µ = 4.47) on a 5-point scale. Eight indicators were excluded due to mean importance ratings falling below the 3.0 threshold. The framework is formed with five main dimensions and 25 concrete indicators. Finally, an AHP-TOPSIS method is used to evaluate the HRC team performance. The AHP analysis reveals that Safety (weight = 0.2708) is prioritized over Productivity (weight = 0.2327) by experts, establishing a safety-first principle for successful HRC deployment. The framework is demonstrated through a case study of a human–robot plastering team, whose team performance scored as fair. This shows that the framework can help practitioners find out the advantages and disadvantages of HRC team performance and provide targeted improvement strategies. Furthermore, the framework offers construction managers a scientific basis for deciding robot deployment and team assignment, thus promoting safer, more efficient, and more creative HRC in construction projects. Full article

The Norwegian construction sector has long struggled with conflict, particularly in large-scale and complex projects, where adversarial practices, rigid procurement systems, and insufficient early collaboration are common. This study explores how partnering contracts, which are collaborative delivery models designed to align stakeholder interests, affect conflict dynamics in real-world settings. Employing a mixed-methods approach, it combines semi-structured interviews with 21 experienced Norwegian construction professionals and a structured survey of 33 industry experts. The findings reveal that partnering can foster trust, improve communication, and reduce adversarial behavior through mechanisms such as early contractor involvement, joint goal setting, and open dialogue. However, participants also identified critical risks: superficial collaboration rituals, ambiguous roles, and unresolved structural inequalities that can exacerbate tensions. Importantly, the study emphasizes that partnering success depends less on the contract itself and more on cultural alignment, stakeholder competence, and long-term relational commitment. These insights contribute to a more nuanced understanding of how collaborative contracting influences conflict mitigation in the Norwegian construction sector. Full article

Building information modeling (BIM) has emerged as a fundamental component of Industry 4.0 recently. BIM construction engineers (BCEs) play a pivotal role in implementing BIM, and their personal competency is crucial to the successful application and promotion of BIM technology. Existing research on evaluating BIM capabilities has mainly focused on the enterprise or project level, neglecting individual-level analysis. Therefore, this study aims to establish an individual-level competency evaluation model for BCEs. Firstly, the competency of BCEs was divided into five levels by referring to relevant standards and domestic and foreign research. Secondly, through the analysis of literature data and website data, the competency evaluation indicator system for BCEs was constructed, which includes four primary indicators and 27 secondary indicators. Thirdly, variable weight theory was used to optimize the weights determined by general methods and calculate the comprehensive weights of each indicator. Then the competency levels of BCEs were determined by the interval grey clustering method. To demonstrate the application of the proposed method, a case study from a Chinese enterprise was conducted. The main results derived from this case study are as follows: domain competencies have the greatest weight among the primary indicators; the C9-BIM model is the secondary indicator with the highest weight (${\mathsf{\omega }}_j$ = 0.0804); and the competency level of the BCE is “Level 3”. These results are consistent with the actual situation of the enterprise. The proposed model in this study provides a comprehensive tool for evaluating BCEs’ competencies from an individual perspective, and offers guideline for BCEs to enhance their competencies in pursuing sustainable professional development. Full article

Managed aquifer recharge (MAR) is a complex and hidden process of storing surplus water under the ground surface and extracting it as, when and where needed. Evaluation of the success of any MAR project is challenging due to uncertainty in estimating the hydrogeological characteristics of the subsurface media. This paper demonstrates the use of a groundwater model (MODFLOW) to evaluate a new, large-scale regional MAR project in the agricultural heartland in Punjab, Pakistan. In this MAR project, flood waters have been diverted to the bed of an abandoned canal, where 144 recharge wells (the wells for accelerating the recharge into the aquifer) have been constructed to accelerate the recharge to the aquifer. The model was calibrated for a period of five years from October 2015 to June 2020 on a monthly stress period and the resulting water levels were simulated till 2035. The water balance components and future response of the aquifer to different scenarios up to 2035 including with and without MAR situations are presented. The model simulations showed that MAR can contribute to the replenishment of the aquifer and its potential for the case study site to contribute significantly to the management of groundwater and to enhance supplies for intensive agriculture. It was further established that MODFLOW can help in the evaluation of effectiveness of a MAR scheme. This study is unique as it evaluates a significantly large MAR project in an area where this practice has not been developed for improving groundwater access for large scale irrigation. The model provides guidelines for decision makers in the region as well as for the global community and livelihood benefits for rural communities. Full article

The degradation of edible fungi can lead to a decrease in cultivation yield and economic losses. In this study, a nondestructive detection method for strain degradation based on the fusion of hyperspectral technology and image texture features is presented. Hyperspectral and microscopic image data were acquired from Pleurotus geesteranus strains exhibiting varying degrees of degradation, followed by preprocessing using Savitzky–Golay smoothing (SG), multivariate scattering correction (MSC), and standard normal variate transformation (SNV). Spectral features were extracted by the successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS), and principal component analysis (PCA), while the texture features were derived using gray-level co-occurrence matrix (GLCM) and local binary pattern (LBP) models. The spectral and texture features were then fused and used to construct a classification model based on convolutional neural networks (CNN). The results showed that combining hyperspectral and image texture features significantly improved the classification accuracy. Among the tested models, the CARS + LBP-CNN configuration achieved the best performance, with an overall accuracy of 95.6% and a kappa coefficient of 0.96. This approach provides a new technical solution for the nondestructive detection of strain degradation in Pleurotus geesteranus. Full article

Building Information Modeling (BIM) is an emerging information technology tool and management concept in the construction industry, enabling the transition from traditional 2D drawings to 3D models. It helps improve efficiency and promote industrial upgrading in the construction sector. However, in actual project practices, the effectiveness of BIM application has not been as expected, and the return on investment (ROI) may even be negative. Through a literature review, we found that risk identification, correlation analysis, and risk assessment related to BIM implementation require further research. To better promote the application of BIM in the construction industry, this study employs relevant methods to analyze the risk factors of BIM implementation. Through the literature review, 31 BIM implementation risk factors were identified, and 24 major risk factors were extracted using the AHP (Analytic Hierarchy Process) method. The ISM (Interpretative Structural Modeling) method was then used to determine the interrelationships among these major risk factors, establishing a hierarchical model with seven levels. Through MICMAC (Matrices Impacts Corises-Multiplication Appliance Classment) analysis, the BIM implementation risk factors were categorized into three groups, and three-tiered response strategies were proposed at the industry, organizational, and project levels. By analyzing the main risk factors of BIM application in China’s construction industry and formulating corresponding response strategies to promote its successful application, this study contributes to the knowledge system. The findings also provide a reference for other countries and regions to clarify major risk factors and their interrelationships, thereby improving the effectiveness of BIM implementation. Full article

Timely and informed decision-making is essential for the successful execution of construction projects, where delays and cost overruns frequently pose significant risks. Earned value analysis (EVA) provides a robust, integrated framework that combines scope, schedule, and cost performance to support proactive project control. This study investigates the effectiveness of EVA as a decision-support tool by applying it to two real-life construction case studies. Key performance indicators, including Cost Performance Index (CPI), Schedule Performance Index (SPI), Estimate at Completion (EAC), and Estimate to Complete (ETC), are calculated and analyzed over a specific monitoring period. The analysis revealed a 15.36% cost savings and a 10.42% schedule improvement during the monitored period. By comparing planned and actual performance data, the study demonstrates how EVA enables early detection of deviations, thereby empowering project managers to implement timely corrective actions. The findings highlight EVA’s practical utility in improving project transparency, enhancing cost and schedule control, and supporting strategic decision-making in real-world construction environments. Full article

The determination of precise and reliable interior (IO) and relative (RO) orientation parameters for thermal infrared (TIR) cameras is critical for their subsequent use in photogrammetric processes. Although 2D calibration boards have become the predominant approach for TIR geometric calibration, these targets are susceptible to projective coupling and often introduce error through manual construction methods, necessitating the development of 3D targets tailored to TIR geometric calibration. Therefore, this paper evaluates TIR geometric calibration results obtained from 2D board and 3D field calibration approaches, documenting the construction, observation, and calculation of IO and RO parameters. This includes a comparative analysis of values derived from three popular commercial software packages commonly used for geometric calibration: MathWorks’ MATLAB, Agisoft Metashape, and Photometrix’s Australis. Furthermore, to assess the validity of derived parameters, two InfraRed Thermography 3D-Data Fusion (IRT-3DDF) methods are developed to model historic building façades and medieval frescoes. The results demonstrate the success of the proposed 3D field calibration targets for the calculation of both IO and RO parameters tailored to photogrammetric data fusion. Additionally, a novel combined TIR-RGB bundle block adjustment approach demonstrates the success of applying ‘out-of-the-box’ deep-learning neural networks for multi-modal image matching and thermal modelling. Considerations for the development of TIR geometric calibration approaches and the evolution of proposed IRT-3DDF methods are provided for future work. Full article

Market-oriented ecological restoration is vital for advancing ecological civilization and promoting harmonious human–nature relationships. However, the precise implementation pathway remains unclear. Few studies specifically address challenges that arise during ecological restoration implementation. Ensuring the smooth and effective implementation and landing of ecological restoration projects harmonizes ecological and economic objectives at the regional scale and fosters sustainable development in the region. Based on the policies of market-oriented ecological restoration collected from various Chinese provinces, and through multi-level institutional analysis, the policy measures are categorized into three phases: early, middle, and late. For each phase, we summarize the challenges encountered in implementing market-oriented ecological restoration projects. Finally, by the method of constructing theoretical models, we propose sustainable countermeasures based on multiple theoretical models. The results show (1) China’s ecological restoration sector is experiencing rapid growth, and market-oriented policies in China, multiple Chinese provinces, and municipalities have enacted successive market-oriented ecological restoration policies, and the outlook for ecological restoration marketization in China remains highly promising. (2) The implementation process of current market-oriented ecological restoration projects confronts and encounters several challenges. These include the absence of project screening and evaluation mechanisms, limited investment and financing channels, ill-defined approval processes, ambiguous delineation of departmental responsibilities, insufficient industry incentives, and the absence of effective operational and management mechanisms. (3) To address the identified challenges, taking forest ecological restoration as an example, theoretical models should be developed encompassing six critical dimensions: the aspects of the mechanism, mode, approval process, management system, industrial chain, and platform. This aims to provide sustainable pathways for the effective implementation of market-oriented forest ecological restoration projects. Full article

Engineering contract management is a critical component of project management systems, serving as a key mechanism for ensuring successful project implementation. This study systematically analyzes 349 s-instance judicial cases related to construction engineering contract disputes in the Yangtze River Delta Economic Zone from 2017 to 2021, based on data obtained from the China Judgments Online database. The research identifies contractual risk characteristics across dimensions such as regional distribution, dispute terminology, legal citation patterns, and appellate role transitions. The key findings include the following: (1) Primary risks involve payment disputes, quality assurance failures, contractual validity issues, and schedule compliance challenges. (2) Litigation patterns reveal complex interdependencies between contracting parties and stakeholders, posing significant risk management challenges. (3) High second-instance modification rates stem from procedural irregularities, new evidence, improper legal application, and factual errors in initial trials. The study proposes stratified risk mitigation strategies, including governmental regulatory improvements and enterprise-level management optimizations. These findings offer valuable insights into advancing risk governance in construction contract administration, particularly through an enhanced understanding of dispute complexity and systemic vulnerabilities. Full article

Urban green infrastructure (UGI) projects rely on collaboration and involve a diverse team of professionals, including constructors, designers, green builders, and maintenance staff. This socially oriented case study focuses on the relational dynamics among UGI professionals, their roles in landscape construction processes, and how these relationships can influence the project’s success and its capacity to implement carbon-smart solutions. “Carbon-smart solutions” refers here to practices aimed at maximising carbon sequestration and storage while minimising carbon emissions. Data for this study were collected through semi-structured in-depth interviews and analysed using deductive qualitative analysis. A coding framework, investigator triangulation, and a representative sample of various professionals were employed to confirm the data’s validity. This study identified several relational factors that either challenge or drive the project’s success and carbon smartness. At the interpersonal level, the determinant drivers and challengers in UGI professionals’ relations were linked to the definition of working roles, power dynamics, the building of mutual trust through open communication, and the possession of the necessary sustainability skills. At the institutional level, relations concerning the shared principles and rationales of the project, as well as the project design process and diverse working cultures, presented both constraints and advances in project success and carbon-smart solutions. Full article

Pipe-jacking construction technology is favored in urban construction due to its advantages of high safety and being a non-excavation technique. However, instability of the tunnel face often occurs due to unfavorable conditions, such as pipe jacking across the river channel, shallow soil cover, and improper control of the support pressure. In this study, we made a use of the limit balance method and mud–water balance theory. At this moment of passive damage and active destruction occurring at the pipe-jacking tunnel face, the general mathematical expressions of the tunnel-face support pressure (with lower limit value P_min and upper limit value P_max) are derived. In the non-river impact area and river impact area, the optimal value P_o of support pressure at the tunnel face is thus derived. Then, based on the Y25-Y26 pipe-jacking project across the Chu River channel in Hefei North District, a numerical simulation method is used to support further discussion. The results indicate that, when the river overburden is 3 m, the ultimate support pressure calculated by means of numerical simulation is 881.786 kN, and the optimal support ratio $\mathsf{\lambda }$ is taken in the interval of 1.0~1.5. Secondly, the upper limit value P_max, lower limit value P_min, and optimum value P_o calculated using the theoretical equations are 2669.977 kN, 309.910 kN, and 1044.870 kN, respectively. These results leads us to recommend setting the support pressure of the tunnel face in a reasonable range between the upper limit value P_max and the lower limit value P_min, to ensure that the tunnel-face support pressure and resistance during pipe jacking always remain in a balanced state. The relevant research results from this study provide an important technical guarantee for the successful implementation of the examined project and, at the same time, can serve as a reference example for similar projects. Full article

This study addresses the urgent need for the rapid, non-destructive assessment of key soybean components, including moisture, fat, and protein, using near-infrared (NIR) spectroscopy. This study provides technical and theoretical support for achieving the efficient and accurate detection of major soybean components and for the development of portable near-infrared (NIR) instruments. Thirty soybean samples from diverse sources were collected, and 360 spectral measurements were acquired using a 900–1700 nm NIR spectrometer after grinding and standardized sampling. To improve model robustness, preprocessing strategies such as standard normal variate (SNV), multiplicative scatter correction (MSC), and Savitzky–Golay derivatives were applied. Feature selection was conducted using competitive adaptive reweighted sampling (CARS), successive projections algorithm (SPA), and uninformative variable elimination (UVE), followed by model construction with partial least squares regression (PLSR), support vector regression (SVR), and random forest (RF). Comparative analysis revealed that the RF model consistently outperformed the others across most combinations. Specifically, the SPASNV + D₁–RF combination achieved an RPD of 14.7 for moisture, CARS–SNV + D₁–RF reached 5.9 for protein, and CARS–SG + D₂–RF attained 12.0 for fat, all significantly surpassing alternative methods and demonstrating a strong nonlinear learning capacity and predictive precision. These findings show that integrating optimal preprocessing and feature selection strategies can markedly enhance the predictive accuracy in NIR-based soybean analyses. The RF model offers exceptional stability and performance, providing both technical reference and theoretical support for the development of portable NIR devices and practical rapid-quality assessment systems for soybeans in industrial applications. Full article

With the acceleration of urbanization, the development of urban rail transit networks has become an essential component of modern urban transportation. The construction of new urban rail transit lines often involves crossing existing operational lines, posing significant safety risks and technical challenges. This paper presents a comprehensive study on the safety risk assessment and control measures for the construction of new double-line shield tunnels crossing beneath existing tunnels in complex strata, using the project of Line 5 of the Nanning Urban Rail Transit crossing beneath the existing Line 2 interval tunnel as a case study. This study employs methods such as status investigation, numerical simulation, and field measurement to analyze the construction risks. Key findings include the successful identification and control of major risk sources through refined risk assessment and comprehensive technical measurement. The maximum settlement of the existing tunnel was effectively controlled at −2.55 mm, well within the deformation monitoring control values. This study demonstrates that optimized shield machine selection, improved lining design, interlayer soil reinforcement, the dynamic adjustment of shield parameters, and the precise measurement of shield posture significantly enhance the efficiency of shield tunneling and construction safety. The results provide a valuable reference for the settlement and deformation control of similar projects. Full article