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Seepage of the dam is an important safety problem, which may cause internal erosion of the structure. In the field of seepage monitoring in civil engineering, the distributed optical fiber sensing technology based on the temperature tracing method has been paid more attention due to its unique advantages of high sensitivity, good stability and high resolution. The purpose of this paper is to make a review of the existing related research, so as to facilitate the later scholars to understand and further study more systematically.

In this paper, three kinds of commonly used distributed fiber temperature measurement technologies are introduced. Based on the working principle, monitoring system, theoretical analysis, experimental research and engineering application of the fiber seepage monitoring technology, the present situation of dam seepage monitoring based on distributed fiber is reviewed in detail and their advantages and disadvantages are compared.

The thermal monitoring technology of seepage measurement depends on the accuracy of optical fiber temperature measurement (including the accuracy of the system and the rationality of the discrimination method), the correct installation of optical fiber and the quantitative analysis of temperature data. The accuracy of the current monitoring system can basically meet the existing measurement requirements, but the correct installation of optical fiber and the calibration of temperature data need to be further studied for different discrimination methods, and this field has great research value.

At present, there are many applications and research studies of optical fiber sensing in the field of structural health monitoring, and there are also reviews of related aspects. However, there is little or no review only in the field of seepage monitoring. This paper summarizes the research and application of optical fiber sensing in the field of seepage monitoring. The possibility of the gradient method to find its new prospect with the development of monitoring systems and the improvement of temperature resolution is discussed. The idea of extending the seepage monitoring method based on distributed optical fiber thermal monitoring technology to other monitoring fields is also given in the paper.

The swivel construction method is a specially designed process used to build bridges that cross rivers, valleys, railroads and other obstacles. To carry out this construction method safely, real-time monitoring of the bridge rotation process is required to ensure a smooth swivel operation without collisions. However, the traditional means of monitoring using Electronic Total Station tools cannot realize real-time monitoring, and monitoring using motion sensors or GPS is cumbersome to use.

This study proposes a monitoring method based on a series of computer vision (CV) technologies, which can monitor the rotation angle, velocity and inclination angle of the swivel construction in real-time. First, three proposed CV algorithms was developed in a laboratory environment. The experimental tests were carried out on a bridge scale model to select the outperformed algorithms for rotation, velocity and inclination monitor, respectively, as the final monitoring method in proposed method. Then, the selected method was implemented to monitor an actual bridge during its swivel construction to verify the applicability.

In the laboratory study, the monitoring data measured with the selected monitoring algorithms was compared with those measured by an Electronic Total Station and the errors in terms of rotation angle, velocity and inclination angle, were 0.040%, 0.040%, and −0.454%, respectively, thus validating the accuracy of the proposed method. In the pilot actual application, the method was shown to be feasible in a real construction application.

In a well-controlled laboratory the optimal algorithms for bridge swivel construction are identified and in an actual project the proposed method is verified. The proposed CV method is complementary to the use of Electronic Total Station tools, motion sensors, and GPS for safety monitoring of swivel construction of bridges. It also contributes to being a possible approach without data-driven model training. Its principal advantages are that it both provides real-time monitoring and is easy to deploy in real construction applications.

The activities using drum resources restrict the operation of multi-project systems. However, existing monitoring methods are not suitable for the characteristics of drum activities in the multi-project system. The authors therefore propose an adaptive capacity constraint buffer monitoring model based on the attributes of drum activities, aiming to build a high-efficiency progress control framework for multiple projects.

Considering the attributes and the interrelationship of drum activities, the monitoring reference points are determined on the basis of decentralized buffers. The authors next set action thresholds according to the relationship between the drum activities' interval margin and buffer consumption, and then the corresponding monitoring measures are taken.

The empirical results show that, compared to the classic methods, the proposed approach can effectively monitor the progress of the drum plan and realize the dual optimization of multi-project duration and cost.

The buffer consumption at the follow-up monitoring time point is neglected when determining the action thresholds. Prediction methods can be introduced to present more all-sided monitoring.

This paper fulfils the dual optimization of multi-project duration and cost. It provides a reference guide for project managers.

A capacity constraint buffer monitoring method suitable for a multi-project environment is produced.

The purpose of this paper is to develop an integrity monitoring method using ERAIM (Extended Receiver Autonomous Integrity Monitoring) for the integrated GNSS/Inertial (Global Navigation Satellite System and inertial navigation system) of general aviation aircraft.

First the tightly integrated GNSS with Strapdown Inertial Navigation System (GNSS/SINS) and the Kalman filter is designed. Then the processing of ERAIM is presented, in which the least‐squares theory is used to calculate the best estimators by integrating the predicted states with measurement states of Kalman filter. Based on the new measurement model, the integrity monitoring for GNSS/inertial system is carried out, including the fault detection, identification, reliability and separability. Lastly, the simulation and analysis for ERAIM vs RAIM are performed to validate the proposed method.

Simulation results show that the ERAIM method is able to detect and identify effectively any type of failure including step failure and ramp failure. Compared to the RAIM method for only GNSS, the ERAIM increases the redundant information and reduces the correlation of test statistics, as well as enhancing the reliability and thus can significantly improve the performance of integrity monitoring.

In safety critical sectors such as aviation, stringent integrity performance requirements must be met. The ERAIM method cannot only be used in integrity monitoring for the integrated GNSS/Inertial system, but also can be applied to only GNSS or other integrated navigation systems for general aviation aircraft.

The paper presents a new integrity monitoring method of ERAIM, which is able to improve the fault detection and identification capabilities significantly by extending GNSS‐used RAIM method into the GNSS/Inertial integrated system.

Driven by the development in sensing techniques and information and communications technology, and their applications in the manufacturing system, data-driven quality control methods play an essential role in the quality improvement of assembly products. This paper aims to review the development of data-driven modeling methods for process monitoring and fault diagnosis in multi-station assembly systems. Furthermore, the authors discuss the applications of the methods proposed and present suggestions for future studies in data mining for quality control in product assembly.

This paper provides an outline of data-driven process monitoring and fault diagnosis methods for reduction in variation. The development of statistical process monitoring techniques and diagnosis methods, such as pattern matching, estimation-based analysis and artificial intelligence-based diagnostics, is introduced.

A classification structure for data-driven process control techniques and the limitations of their applications in multi-station assembly processes are discussed. From the perspective of the engineering requirements of real, dynamic, nonlinear and uncertain assembly systems, future trends in sensing system location, data mining and data fusion techniques for variation reduction are suggested.

This paper reveals the development of process monitoring and fault diagnosis techniques, and their applications in variation reduction in multi-station assembly.

The purpose of the paper is to propose a novel approach, based on grey clustering decision, to fill in an omission of quantitative monitoring parameter selection methods.

The basic monitoring parameter selection criteria and the corresponding calculation methods are presented. Then, the grey clustering decision model for monitoring parameter optimization selection is constructed, and an integrated weight determination method based on analytic hierarchy process (AHP) and information entropy is provided.

Basic principle for monitoring parameter selection is proposed and quantitative description is carried out for selection principle in engineering application. Grey clustering decision‐making model for monitoring parameter optimization selection is established. Comprehensive weight ascertainment method based on AHP and information entropy is provided to make the index weight more scientific.

At system design stage, it is of significance to carry out selection and optimization of monitoring parameters. After the optimization of monitoring parameters is confirmed, measurability analysis and design in parallel are carried out for convenience of timely information feedback and system design revision. Therefore, the system integration efficiency is improved and the cost of research and manufacturing is reduced.

Monitoring parameter optimization selection process based on grey clustering decision‐making model is described and the analysis result shows that the proposed method has certain degree of effectiveness, rationality and universality.

The purpose of this study is to investigate the current construction progress monitoring (CPM) process in relation to the contractual obligations, how project management teams carry out this activity in the field and why teams continue to adopt the current method. The study aims to provide a comprehensive understanding of the current monitoring process and its effectiveness, identify any shortcomings and propose recommendations for improvements that can lead to better project outcomes.

The study conducted semi-structured interviews with 28 construction management practitioners to explore their views on contractual requirements, traditional progress monitoring practices and advanced monitoring methods. Thematic analysis was used to identify existing processes, practices and incentives for advanced monitoring.

Standard construction contracts mandate current progress monitoring practices, which often rely on manual, document-centric and labor-intensive methods, leading to slow and erroneous progress reporting and project delays. Key barriers to adopting advanced tools include rigid contractual clauses, lack of incentives and the absence of reliable automated tools. A holistic automated approach that covers the entire CPM process, from planning to claim management, is needed as a viable alternative to traditional practices.

The study's findings can inform researchers, stakeholders and decision-makers about the existing monitoring practices and contribute to enhancing project management practices.

The study identified contractually mandated progress monitoring processes, traditional methods of collecting, transferring, analyzing and dispensing progress-related information and potential incentives and points of departure towards technologically advanced methods.

The objective of this study was to compare three distinct methods of test monitoring in the context of distance education—non-proctored remote or online tests, traditional face-to-face proctored tests and remotely proctored tests using software—to analyze if the method in which tests are monitored influences the obtained grades.

The experiment was carried out at the postgraduate level in the Master's Degree in Administration program in the modality of distance education, with a total of 296 students during three terms wherein the monitoring method of the final exam varied, keeping the other variables constant. This study used a quantitative method in which the distribution of grades was analyzed; and the grades from each method were tested. Finally, using a multiple linear regression model with dichotomous variables, the impact on students' academic performance with each method was quantified.

The results indicated that the remotely proctored online test grades were seven points lower with respect to the traditional method. This result does not mean that the lower scores in the remote proctored condition were due to better adherence to academic honesty, maybe this could be due to test anxiety, technology interference or a number of other factors that would confound the validity of the final test score.

The results indicated that the non-proctored online test favored the grade in four points with respect to the traditional method.

The authors conclude to support recommending non-proctored online test, this can be a closer substitute to the traditional method than remote application with software monitoring.

Not exist another paper to compare three distinct methods of test monitoring in the context of distance education.

Modern production facilities produce large amounts of data. The computational framework often referred to as big data analytics has greatly improved the capabilities of analyses of large data sets. Many manufacturing companies can now seize this opportunity to leverage their data to gain competitive advantages for continuous improvement. Six Sigma has been among the most popular approaches for continuous improvement. The data-driven nature of Six Sigma applied in a big data environment can provide competitive advantages. In the traditional Six Sigma implementation – define, measure, analyze, improve and control (DMAIC) problem-solving strategy where a human team defines a project ahead of data collection. This paper aims to propose a new Six Sigma approach that uses massive data generated to identify opportunities for continuous improvement projects in a manufacturing environment in addition to human input in a measure, define, analyze, improve and control (MDAIC) format.

The proposed Six Sigma strategy called MDAIC starts with data collection and process monitoring in a manufacturing environment using system-wide monitoring that standardizes continuous, attribute and profile data into comparable metrics in terms of “traffic lights.” The classifications into green, yellow and red lights are based on pre-control charts depending on how far a measurement is from its target. The proposed method monitors both process parameters and product quality data throughout a hierarchical production system over time. An attribute control chart is used to monitor system performances. As the proposed method is capable of identifying changed variables with both spatial and temporal spaces, Six Sigma teams can easily pinpoint the areas in need to initiate Six Sigma projects.

Based on a simulation study, the proposed method is capable of identifying variables that exhibit the biggest deviations from the target in the Measure step of a Six Sigma project. This provides suggestions of the candidates for the improvement section of the proposed MDAIC methodology.

This paper proposes a new approach for the identifications of projects for continuous improvement in a manufacturing environment. The proposed framework aims to monitor the entire production system that integrates all types of production variables and the product quality characteristics.

The unique nature of the construction sector makes it fall behind other sectors in terms of productivity. Monitoring construction productivity is crucial for the construction project's success. Current practices for construction productivity monitoring are time-consuming, manned and error prone. Although previous studies have been implemented toward reducing these limitations, a gap still exists in the automated monitoring of construction productivity.

This study aims to investigate and assess the different techniques used for monitoring productivity in building construction projects. Therefore, a mixed review methodology (bibliometric analysis and systematic review) was adopted. All the related publications were collected from different databases, which were further screened to get the most relevant based on the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) criteria.

A detailed review was performed, and it was found that traditional methods, computer vision-based and photogrammetry are the most adopted data acquisition for productivity monitoring of building projects, respectively. Machine learning algorithms (ANN, SVM) and BIM were integrated with monitoring tools and technologies to enhance the automated monitoring performance in construction productivity. Also, it was observed that current studies did not cover all the complex construction job sites and they were applied based on a small sample of construction workers and machines separately.

This review paper contributes to the literature on construction management by providing insight into different productivity monitoring techniques.