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The interaction of systems through a designated control channel has improved communication, efficiency, management, storage, processing, etc. across several industries. The construction industry is an industry that thrives on a well-planned workflow rhythm; a change in the environmental dynamism will either have a positive or negative impact on the output of the project planned for execution. More so, raising the need for effective collaboration through workflow and project planning, grid application in construction facilitates the relationship between the project reality and the end users, all with the aim of improving resources and value management. However, decentralisation of close-domain control can cause uncertainty and incompleteness of data. And this can be a big factor, especially when a complex project is being executed.

Quality failures in grid infrastructure construction would cause large-scale collapses in power supply and additional expenditures by reworks and repairs. Learning from quality failures (LFQF) extracts experience from previous quality events and converts them into preventive measures to reduce or eliminate future construction quality issues. This study aims to investigate the influence factors of LFQF in the construction of grid infrastructure.

The related factors of LFQF, including quality management (QM) practices, quality rectification, and individual learning, were identified by reviewing literature about organizational learning and extracting experience from previous failures. A questionnaire survey was distributed to the grid companies in North, Northeast, Northwest, East, Central, and Southwest China. 381 valid responses collected and analyzed using structural equation modeling (SEM) to test the influence of these factors on LFQF.

The SEM results support that QM practices positively affect individual learning and LFQF. Quality rectification indirectly impacts LFQF via individual learning, while the results did not support the direct link between quality rectification and LFQF.

The findings strengthen practical insights into extracting experience from poor-quality issues and continuous improvement. The contributory factors of LFQF found in this study benefit the practitioners by taking effective measures to enhance organizational learning capability and improve the long-term construction quality performance in the grid infrastructure industry.

Existing research about the application of LFQF still stays at the explorative and conceptual stage. This study investigates the related factors of LFQF, including QM practices, quality rectification, and individual learning, extending the model development of learning from failures (LFF) in construction QM.

Finite element solutions of improved quality are obtained by optimizing the location of nodes of the finite element grid, while keeping the number of degrees of freedom fixed. The formulation of the grid optimization problem is based on the reduction of error associated with interpolation of the exact solution, using functions from the finite element space. Element sizes are selected as design variables: length in R and area in R. Approximate optimal conditions are introduced to obtain a set of operationally useful equations that can be used as guidelines for construction of improved grids. Example problems are given for illustrations.

There are thousands of areas excluded from using electrical energy in China. It is mainly because that these places, which are away from towns, have the characteristics of scattered living and low-power consumption and are difficult to construct the power grid. The utilization of energy in remote areas could improve the level of education and quality of life for people living in there, which has great social significance. However, how to choose the optimal power generation model quantitatively according to local energy advantages is a difficult problem.

To carry out a better assessment of the energy benefits of Chinese rural areas to assist the decision-making of energy utilization project, this paper takes Sunan Yugu Autonomous County in Gansu Province as an example. Four feasible energy utilization scenarios are proposed by analyzing its geographical conditions and re-source advantage, respectively, are photovoltaic power generation, biomass power generation, wind power generation and power grid extension. Based on the above scenarios, the evaluation index system of comprehensive utilization of energy in remote areas is constructed, and the comprehensive benefit of each model is evaluated by adopting entropy-based fuzzy comprehensive evaluation model.

Evaluation results show that the comprehensive benefits of photovoltaic power generation is the best, followed by power grid extension. Thus, preference should be given to the two models in the energy utilization in Sunan County. This evaluation model can provide a scientific reference for the selection decision-making of energy utilization project, which is helpful to provide the feasibility and efficiency of the construction of energy utilization project.

The authors construct the comprehensive benefit evaluation index system and evaluate the comprehensive benefits of different scenarios are by using entropy - fuzzy comprehensive evaluation model. Then the qualitative problem can be analyzed quantitatively. The purpose of this study is to support the decision-making of energy investment. Simultaneously, the paper also has some practical significance in improving the credibility of the government and the quality of local people’s life.

This paper aims to balance the total energy consumption and the transmission delay for data gathering application in wireless sensor networks.

This paper adopts a hierarchical grid structure to reduce the total energy consumption, and utilizes a tree architecture to decrease the transmission delay.

In the results, the proposed method performs better, in terms of the number of rounds and the energy × delay cost, than other data gathering protocols with different network sizes and node densities. Moreover, the proposed method also provides good coverage preservation in different environments.

In this paper, sensor nodes are assumed to be uniformly distributed, homogenous, energy‐constrained. Each sensor node also has ability to adjust its transmission power. For practice, the proposed method needs location information of sensor nodes and the radio interference between sensor nodes during data transmissions should be considered.

The proposed method can significantly reduce the delay time and may be suitable for real‐time data gathering applications.

This paper combines hierarchical grid structure with tree architecture to minimize the energy × delay cost for data gathering application.

Laser point clouds are a 3D reconstruction method with wide range, high accuracy and strong adaptability. Therefore, the purpose is to discover a construction point cloud extraction method that can obtain complete information about the construction of rebar, facilitating construction quality inspection and tunnel data archiving, to reduce the cost and complexity of construction management.

Firstly, this paper analyzes the point cloud data of the tunnel during the construction phase, extracts the main features of the rebar data and proposes an M-E-L recognition method. Secondly, based on the actual conditions of the tunnel and the specifications of Chinese tunnel engineering, a rebar model experiment is designed to obtain experimental data. Finally, the feasibility and accuracy of the M-E-L recognition method are analyzed and tested based on the experimental data from the model.

Based on tunnel morphology characteristics, data preprocessing, Euclidean clustering and PCA shape extraction methods, a M-E-L identification algorithm is proposed for identifying secondary lining rebars in highway tunnel construction stages. The algorithm achieves 100% extraction of the first-layer rebars, allowing for the three-dimensional visualization of the on-site rebar situation. Subsequently, through data processing, rebar dimensions and spacings can be obtained. For the second-layer rebars, 55% extraction is achieved, providing information on the rebar skeleton and partial rebar details at the construction site. These extracted data can be further processed to verify compliance with construction requirements.

This paper introduces a laser point cloud method for double-layer rebar identification in tunnels. Current methods rely heavily on manual detection, lacking objectivity. Objective approaches for automatic rebar identification include image-based and LiDAR-based methods. Image-based methods are constrained by tunnel lighting conditions, while LiDAR focuses on straight rebar skeletons. Our research proposes a 3D point cloud recognition algorithm for tunnel lining rebar. This method can extract double-layer rebars and obtain construction rebar dimensions, enhancing management efficiency.

The purpose of this paper is to provide a comprehensive work flow for the improvement of the Reverse Engineering (RE) process in producing non‐uniform rational B‐splines (NURBS) models from scanned point cloud data. This should become a reliable guide in the creation of desired 3D‐CAD models in order to improve efficiency of downstream operations and further to make decisions regarding quality control.

The paper deals with a detailed investigation of operations in achieving an object's accuracy in the data editing phase and data fitting phase that employs the use of a 3D scanner. A case example involving the ShapeGrabber® AI310 laser scanner was used in digitizing the physical object. Operations considered for investigation at the data editing phase include relaxation, decimation of triangles and sharpening of edges. Contour detection, construct patches, target patch count, grid construction and grid resolution are selected as the operations for investigation in the data fitting phase. Evaluation of the generated digitized models was carried out by performing tests which include 3D Comparisons and Geometric Dimensioning and Tolerance (GD & T) testing.

The process of data editing is considered to be extremely time consuming which requires a high degree of skill in order to carry out the data manipulation steps. For the purpose of investigation, an electrical socket cover was considered as the object for digitization. The study found some contributors to enhance the quality of the digital model that can be used in the first piece inspection. The results indicate that although the operations associated with the data fitting phase affect the overall quality of the digitized model; they are however, limited by whatever the quality achieved at the data editing phase.

The RE work flow described in this research will assist designers and practitioners in improving both the efficiency and effectiveness of design and manufacturing functions.

The data editing and fitting processes are time consuming due to various adjustments necessary in obtaining a NURBS model from the digitized data. Thus, the proposed RE work flow identified the steps to realize the desired CAD models from the point cloud data. Moreover, from this study, practitioners will get a concise overall understanding about which geometrical features need to be adjusted so that the required model can be achieved; instead of the need to develop this procedure by themselves through the process of trial and error.