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Cost estimating process is an important element within the project life cycle. Comprehensive information, expanded knowledge, considerable expertise, and continuous improvement are needed to obtain accurate cost estimation. The purpose of this paper is to identify the critical factors that affect accuracy of cost estimation and evaluate the degree to which these factors are important from contractors’ and consultants’ viewpoints.

Qualitative and quantitative research approaches were adopted in collecting and analyzing the data, and testing the hypotheses. Based on the literature review, a questionnaire was prepared and then was modified according to the results of face-to-face open-ended interviews conducted with 11 project managers. The final version of the questionnaire was distributed to a random sample of 265 respondents. For analyzing the collected data Kendall’s and Mann-Whitney tests were conducted.

The analysis revealed that there is a strong agreement between contractors and consultants in the ranking of the factors related to consultant, contractor, design parameters, and information. A slightly weak agreement between contractors and consultants was noted regarding the factors related to market conditions (external factors) and factors related to project characteristics. Furthermore, the results show that the top ten factors affecting the accuracy of cost estimate are clear and detail drawings and specification, pricing experience of construction projects, perception of estimation importance, equipment (cost/availability/performance), project complexity, clear scope definition, accuracy and reliability of cost information, site constraints (access, storage, services), material availability, financial capabilities of the client, and availability of database of bids on similar project (historical data).

Offers an original view of the concept of accuracy of cost estimates as it relates to the efficiency of the project relying on both literature review and empirical evidence.

The purpose of this paper is to investigate how management attention and supply chain complexity affect the decision-making process and cost estimation accuracy of supply chain design (SCD) decisions.

The research follows an embedded case study design. Through the lens of the behavioural theory of the firm, the SCD decision process and realised outcomes are investigated through longitudinal data collection across ten embedded cases with varying degrees of supply chain decision-making complexity and management attention.

The findings suggest that as supply chain decision-making complexity increases, cost estimation accuracy decreases. The extent to which supply chain decision-making complexity is readily recognised influences the selection of strategies for information search and analysis and, thus, impacts resulting cost estimation errors. The paper further shows the importance of management attention for cost estimation accuracy, especially management attention based on conflicting goals induce behaviours that improve estimation ability.

A framework proposing a balance between supply chain decision-making complexity and management attention in SCD decisions is proposed. However, as an embedded case study the research would benefit from replication to externally validate results.

The method used in this study can identify how supply chain complexity is related to cost estimation errors and how management attention is associated with behaviours that improve cost estimation accuracy, indicating the importance of management attention in complex supply chain decision-making.

Challenges associated with ensuring the accuracy and reliability of cost estimation of highway construction bid items are of significant interest to state highway transportation agencies. Even with the existing research undertaken on the subject, the problem of inaccurate estimation of highway bid items still exists. This paper aims to assess the accuracy of the cost estimation methods employed in the selected studies to provide insights into how well they perform empirically. Additionally, this research seeks to identify, synthesize and assess the impact of the factors affecting highway unit prices because they affect the total cost of highway construction costs.

This paper systematically searched, selected and reviewed 105 papers from Scopus, Google Scholar, American Society of Civil Engineers (ASCE), Transportation Research Board (TRB) and Science Direct (SD) on conceptual cost estimation of highway bid items. This study used content and nonparametric statistical analyses to determine research trends, identify, categorize the factors influencing highway unit prices and assess the combined performance of conceptual cost prediction models.

Findings from the trend analysis showed that between 1983 and 2019 North America, Asia, Europe and the Middle East contributed the most to improving highway cost estimation research. Aggregating the quantitative results and weighting the findings using each study's sample size revealed that the average error between the actual and the estimated project costs of Monte-Carlo simulation models (5.49%) performed better compared to the Bayesian model (5.95%), support vector machines (6.03%), case-based reasoning (11.69%), artificial neural networks (12.62%) and regression models (13.96%). This paper identified 41 factors and was grouped into three categories, namely: (1) factors relating to project characteristics; (2) organizational factors and (3) estimate factors based on the common classification used in the selected papers. The mean ranking analysis showed that most of the selected papers used project-specific factors more when estimating highway construction bid items than the other factors.

This paper contributes to the body of knowledge by analyzing and comparing the performance of highway cost estimation models, identifying and categorizing a comprehensive list of cost drivers to stimulate future studies in improving highway construction cost estimates.

In order to overcome the uncertainty and improve the accuracy of spectral estimation, this paper aims to establish a grey fuzzy prediction model of soil organic matter content by using grey theory and fuzzy theory.

Based on the data of 121 soil samples from Zhangqiu district and Jiyang district of Jinan City, Shandong Province, firstly, the soil spectral data are transformed by spectral transformation methods, and the spectral estimation factors are selected according to the principle of maximum correlation. Then, the generalized greyness of interval grey number is used to modify the estimation factors of modeling samples and test samples to improve the correlation. Finally, the hyper-spectral prediction model of soil organic matter is established by using the fuzzy recognition theory, and the model is optimized by adjusting the fuzzy classification number, and the estimation accuracy of the model is evaluated using the mean relative error and the determination coefficient.

The results show that the generalized greyness of interval grey number can effectively improve the correlation between soil organic matter content and estimation factors, and the accuracy of the proposed model and test samples are significantly improved, where the determination coefficient R² = 0.9213 and the mean relative error (MRE) = 6.3630% of 20 test samples. The research shows that the grey fuzzy prediction model proposed in this paper is feasible and effective, and provides a new way for hyper-spectral estimation of soil organic matter content.

The research shows that the grey fuzzy prediction model proposed in this paper can not only effectively deal with the three types of uncertainties in spectral estimation, but also realize the correction of estimation factors, which is helpful to improve the accuracy of modeling estimation. The research result enriches the theory and method of soil spectral estimation, and it also provides a new idea to deal with the three kinds of uncertainty in the prediction problem by using the three kinds of uncertainty theory.

The paper succeeds in realizing both the grey fuzzy prediction model for hyper-spectral estimating soil organic matter content and effectively dealing with the randomness, fuzziness and grey uncertainty in spectral estimation.

Previous studies have highlighted that overheads form a critical part of the total project cost. However, despite this knowledge, the precise estimation of overheads is often neglected in construction projects. This paper aims to examine the reasons for the lack of effort in estimation of overheads.

The study is carried out in three stages. In stage one, an introductory survey is carried to understand the importance of overheads in total project cost. In stage two, a detailed survey is carried out to understand the factors that affect the level of accuracy and compromise made in estimation of overheads using partial least squares structural equation modeling (PLS-SEM). In the final stage, two cases are examined in form of interviews to validate the findings. The model is tested for its reliability, validity and goodness of fit.

The findings of the study suggest that the time and cost spent is a critical issue. Therefore, if the projects feel that the benefit cost ratio for time and cost spend in the process is positive, they estimate the overheads accurately, whereas if they feel that the cost benefit ratio for time and cost spend is negative, they compromise with accurate estimation. Further, there is a lot of subjectivity in defining and processing overheads that leads to a negative impact on the accuracy level in estimation of overheads. The contract type also influences the compromise in estimation.

First, there is scant work that has been carried out on understanding the behavior of overheads and reasons for lack of effort in its accurate estimation in construction projects. Therefore, there are no recent citations in the study. Further, the study being exploratory in nature draws conclusions based on opinion expressed by respondents on survey and interview. Finally, the study is geographically limited as the entire respondent's work in India.

Projects should give due attention to accurate estimation of overheads. Accurate estimation of overheads can help in better control of project margins, thereby serving the profit-maximizing goals of organizations. A conscious effort by industry experts, academicians and researchers can bring some discipline in overhead estimation rather than leaving the critical domain only to thumb rules or experiential assumptions. The regulatory bodies and the project management bodies are advised to come up with some kind of ready reference that can quickly help estimators to arrive at accurate overhead costs.

To the best of knowledge, it is a rare study to exclusively focus on project overhead cost in construction industry and focus on its estimation efforts. The study also uses a robust research process, which improves the reliability and validity of its findings.

The cost and duration estimation process is important to be monitored and controlled in construction projects. Estimation variation from actuals presents a problem when attempting to complete a project on planned time and budget. Various studies cover project performance monitoring with different cost and time performance indices. Nevertheless, project monitoring techniques do not take advantage of the available data to assess the performance and accuracy of estimates developed by the estimation team.

This research proposes using statistical process control (SPC) to assess the consistency and stability of the estimation of activities’ costs and durations. The proposed system calculates the deviation of the estimated costs and durations from the actual values. These calculations are the activities’ indices that are used to plot the control chart. The process capability analysis (PCA) is used to determine the accuracy deviation of the estimations from the organization’s targets.

Results of the proposed system application to a real project determine the activities that have inaccurate cost and duration estimations. This result helps the estimation departments to analyze reasons for inaccurate estimations. The proposed system is an easy, effective tool for continuous improvement to the performance of the estimation department.

Some projects are classified as troubled projects when calculating the status of the project with reference to estimations, while the estimations themselves are troubled and need to be corrected. The proposed system of this paper is considered a novel approach by using SPC techniques such as control charts and process capability analysis for continuous monitoring and assessing of cost and duration estimation process performance to improve process accuracy and increase the credibility of estimation teams or departments.

In order to explore a new estimation approach of hyperspectral estimation, this paper aims to establish a hyperspectral estimation model of soil organic matter content with the principal gradient grey information based on the grey information theory.

Firstly, the estimation factors are selected by transforming the spectral data. The eigenvalue matrix of the modelling samples is converted into grey information matrix by using the method of increasing information and taking large, and the principal gradient grey information of modelling samples is calculated by using the method of pro-information interpolation and straight-line interpolation, respectively, and the hyperspectral estimation model of soil organic matter content is established. Then, the positive and inverse grey relational degree are used to identify the principal gradient information quantity of the test samples corresponding to the known patterns, and the cubic polynomial method is used to optimize the principal gradient information quantity for improving estimation accuracy. Finally, the established model is used to estimate the soil organic matter content of Zhangqiu and Jiyang District of Jinan City, Shandong Province.

The results show that the model has the higher estimation accuracy, among the average relative error of 23 test samples is 5.7524%, and the determination coefficient is 0.9002. Compared with the commonly used methods such as multiple linear regression, support vector machine and BP neural network, the hyperspectral estimation accuracy of soil organic matter content is significantly improved. The application example shows that the estimation model proposed in this paper is feasible and effective.

The estimation model in this paper not only fully excavates and utilizes the internal grey information of known samples with “insufficient and incomplete information”, but also effectively overcomes the randomness and grey uncertainty in the spectral estimation. The research results not only enrich the grey system theory and methods, but also provide a new approach for hyperspectral estimation of soil properties such as soil organic matter content, water content and so on.

The paper succeeds in realizing both a new hyperspectral estimation model of soil organic matter content based on the principal gradient grey information and effectively dealing with the randomness and grey uncertainty in spectral estimation.

In order to improve the estimation accuracy of soil organic matter, this paper aims to establish a modified model for hyperspectral estimation of soil organic matter content based on the positive and inverse grey relational degrees.

Based on 82 soil sample data collected in Daiyue District, Tai'an City, Shandong Province, firstly, the spectral data of soil samples are transformed by the first order differential and logarithmic reciprocal first order differential and so on, the correlation coefficients between the transformed spectral data and soil organic matter content are calculated, and the estimation factors are selected according to the principle of maximum correlation. Secondly, the positive and inverse grey relational degree model is used to identify the samples to be identified, and the initial estimated values of the organic matter content are obtained. Finally, based on the difference information between the samples to be identified and their corresponding known patterns, a modified model for the initial estimation of soil organic matter content is established, and the estimation accuracy of the model is evaluated using the mean relative error and the determination coefficient.

The results show that the methods of logarithmic reciprocal first order differential and the first-order differential of the square root for transforming the original spectral data are more effective, which could significantly improve the correlation between soil organic matter content and spectral data. The modified model for hyperspectral estimation of soil organic matter has high estimation accuracy, the average relative error (MRE) of 11 test samples is 4.091%, and the determination coefficient (R²) is 0.936. The estimation precision is higher than that of linear regression model, BP neural network and support vector machine model. The application examples show that the modified model for hyperspectral estimation of soil organic matter content based on positive and inverse grey relational degree proposed in this article is feasible and effective.

The model in this paper has clear mathematical and physics meaning, simple calculation and easy programming. The model not only fully excavates and utilizes the internal information of known pattern samples with “insufficient and incomplete information”, but also effectively overcomes the randomness and grey uncertainty in the spectral estimation of soil organic matter. The research results not only enrich the grey system theory and methods, but also provide a new approach for hyperspectral estimation of soil properties such as soil organic matter content, water content and so on.

The paper succeeds in realizing both a modified model for hyperspectral estimation of soil organic matter based on the positive and inverse grey relational degrees and effectively dealing with the randomness and grey uncertainty in spectral estimation.

The purpose of this paper is to compare the performance of regression and artificial-neural-networks (ANNs) methods to estimate the running cost of building projects towards improved accuracy.

A data set of 20 building projects is used to test the performance of these two (ANNs/regression) models in estimating running cost. The concept of cost-significant-items is identified as important in assisting estimation. In addition, a stepwise technique is used to eliminate insignificant factors in regression modelling. A connection weight method is applied to determine the importance of cost factors in the performance of ANNs.

The results illustrate that the value of the coefficient of determination=99.75 per cent for ANNs model(s), with a value of 98.1 per cent utilising multiple regression (MR) model(s); second, the mean percentage error (MPE) for ANNs at a testing stage is 0.179, which is less than that of the MPE gained through MR modelling of 1.28; and third, the average accuracy is 99 per cent for ANNs model(s) and 97 per cent for MR model(s). On the basis of these results, it is concluded that an ANNs model is superior to a MR model when predicting running cost of building projects.

A means for continuous improvement for the performance of the models accuracy has been established; this may be further enhanced by future extended sample.

This work extends the knowledge base of life-cycle estimation where ANNs method has been found to reduce preparation time consumed and increasing accuracy improvement of the cost estimation.

Preparation of preliminary estimate is difficult owing to the lack of full project details in the early phases of the construction project. This paper seeks to assess the estimation techniques used for road projects and the critical factors affecting their accuracy in the Ghanaian construction industry.

Quantitative research design was adopted and questionnaire was designed to retrieve data. The target population were engineers and quantity surveyors who were contacted using an e-questionnaire through their professional bodies owing to location constraints. Retrieved data were analysed using descriptive and exploratory factor analysis. In order to compare the opinions of the respondents, the Mann–Whitney U-test was employed.

The survey revealed that subjective, parametric, comparative and analytical estimations are in use in Ghana. The most critical factors influencing the accuracy of estimation techniques are improper project planning, insufficient preliminary site investigation and usage of shortcuts, among others.

This study was limited to Accra, Ghana, due to time and distance constraint.

For accuracy of preliminary estimates to be improved, estimators being the custodian of the estimate are expected to be devoid of errors such as arithmetic calculation errors, inaccurate quantity measurement and error of omission. The usage of estimating software can eliminate these human errors.

The study will assist policymakers and stakeholders in aligning mitigative actions for factors influencing preliminary estimate of road projects with defined clusters rather than basic ranks. With attention focussed on the characteristics of each cluster, accuracy of preliminary estimate can be improved.

The safety management of construction machines is of primary importance. Considering that traditional construction machine safety monitoring and evaluation methods cannot adapt to the complex construction environment, and the monitoring methods based on sensor equipment cost too much. This paper aims to introduce computer vision and deep learning technologies to propose the YOLOv5-FastPose (YFP) model to realize the pose estimation of construction machines by improving the AlphaPose human pose model.

This model introduced the object detection module YOLOv5m to improve the recognition accuracy for detecting construction machines. Meanwhile, to better capture the pose characteristics, the FastPose network optimized feature extraction was introduced into the Single-Machine Pose Estimation Module (SMPE) of AlphaPose. This study used Alberta Construction Image Dataset (ACID) and Construction Equipment Poses Dataset (CEPD) to establish the dataset of object detection and pose estimation of construction machines through data augmentation technology and Labelme image annotation software for training and testing the YFP model.

The experimental results show that the improved model YFP achieves an average normalization error (NE) of 12.94 × 10^–3, an average Percentage of Correct Keypoints (PCK) of 98.48% and an average Area Under the PCK Curve (AUC) of 37.50 × 10^–3. Compared with existing methods, this model has higher accuracy in the pose estimation of the construction machine.

This study extends and optimizes the human pose estimation model AlphaPose to make it suitable for construction machines, improving the performance of pose estimation for construction machines.

Estimating is a fundamental part of the construction industry. The success or failure of a project is dependent on the accuracy of several estimates through‐out the course of the project. Construction estimating is the compilation and analysis of many items that influence and contribute to the cost of a project. Estimating which is done before the physical performance of the work requires a detailed study and careful analysis of the bidding documents, in order to achieve the most accurate estimate possible of the probable cost consistent with the bidding time available and the accuracy and completeness of the information submitted. Overestimated or underestimated cost has the potential to cause loss to local contracting companies. The objective of this paper is to identify the essential factors and their relative importance that affect accuracy of cost estimation of building contracts in the Gaza strip. The results of analyzing fifty one factors considered in a questionnaire survey concluded that the main factors are: location of the project, segmentation of the Gaza strip and limitation of movements between areas, political situation, and financial status of the owner.

The purpose of this paper is to identify factors affecting cost estimate accuracy in each of five design stages preceding building construction: order of magnitude, conceptual/schematic, detailed design, construction document and bid phase.

Data were collected using an online survey completed by 138 respondents who work in design consultancy firms in Jordan, including project managers, architects and quantity surveyors (QSs). Survey responses were analyzed using descriptive statistics. Confirmatory interviews and case study comparisons were used to confirm the statistical analysis results.

The results of this study indicated that each design stage’s cost estimate was affected by several factors. Two significant factors were common across four of the five design stages: client experience and project team experience. In addition, a high level of agreement was observed among the project managers, architects and QSs regarding the factors affecting cost estimate accuracy.

Accurately estimating building construction costs during the design process has posed a challenge for designers and their clients in Jordan. Despite the care and effort involved in preparing cost estimates in each of the five design stages, deviations are commonly observed. Because the accuracy of building construction cost estimates directly affect the success or failure of a project, the results of this study can be used to reduce uncertainties in building construction cost estimation and subsequently increase the likelihood of project success

Wheeled robots have been widely used in People’s Daily life. Accurate positioning is the premise of autonomous navigation. In this paper, an optimization-based visual-inertial-wheel odometer tightly coupled system is proposed, which solves the problem of failure of visual inertia initialization due to unobservable scale.The aim of this paper is to achieve robust localization of visually challenging scenes.

During system initialization, the wheel odometer measurement and visual-inertial odometry (VIO) fusion are initialized using maximum a posteriori (MAP). Aiming at the visual challenge scene, a fusion method of wheel odometer and inertial measurement unit (IMU) measurement is proposed, which can still be robust initialization in the scene without visual features. To solve the problem of low track accuracy caused by cumulative errors of VIO, the local and global positioning accuracy is improved by integrating wheel odometer data. The system is validated on a public data set.

The results show that our system performs well in visual challenge scenarios, can achieve robust initialization with high efficiency and improves the state estimation accuracy of wheeled robots.

To realize robust initialization of wheeled robot, wheel odometer measurement and vision-inertia fusion are initialized using MAP. Aiming at the visual challenge scene, a fusion method of wheel odometer and IMU measurement is proposed. To improve the accuracy of state estimation of wheeled robot, wheel encoder measurement and plane constraint information are added to local and global BA, so as to achieve refined scale estimation.

The purpose of this paper is to propose a multi-feature, multi-metric and multi-loop tightly coupled LiDAR-visual-inertial odometry, M³LVI, for high-accuracy and robust state estimation and mapping.

M³LVI is built atop a factor graph and composed of two subsystems, a LiDAR-inertial system (LIS) and a visual-inertial system (VIS). LIS implements multi-feature extraction on point cloud, and then multi-metric transformation estimation is implemented to realize LiDAR odometry. LiDAR-enhanced images and IMU pre-integration have been used in VIS to realize visual odometry, providing a reliable initial guess for LIS matching module. Location recognition is performed by a dual loop module combined with Bag of Words and LiDAR-Iris to correct accumulated drift. M³LVI also functions properly when one of the subsystems failed, which greatly increases the robustness in degraded environments.

Quantitative experiments were conducted on the KITTI data set and the campus data set to evaluate the M³LVI. The experimental results show the algorithm has higher pose estimation accuracy than existing methods.

The proposed method can greatly improve the positioning and mapping accuracy of AGV, and has an important impact on AGV material distribution, which is one of the most important applications of industrial robots.

M³LVI divides the original point cloud into six types, and uses multi-metric transformation estimation to estimate the state of robot and adopts factor graph optimization model to optimize the state estimation, which improves the accuracy of pose estimation. When one subsystem fails, the other system can complete the positioning work independently, which greatly increases the robustness in degraded environments.

This study aims to improve the accuracy of hyperspectral estimation of soil organic matter content.

Based on the uncertainty in spectral estimation, 76 soil samples collected in Zhangqiu District, Jinan City, Shandong Province, were studied in this paper. First, the spectral transformation of the spectral data after denoising was carried out by means of 11 transformation methods such as reciprocal and square, and the estimation factor was selected according to the principle of maximum correlation. Secondly, the grey weighted distance was used to calculate the grey relational degree between the samples to be estimated and the known patterns, and the local linear regression estimation model of soil organic matter content was established by using the pattern samples closest to the samples to be identified. Thirdly, the models were optimized by gradually increasing the number of modeling samples and adjusting the decision coefficient, and a comprehensive index was constructed to determine the optimal predicted value. Finally, the determination coefficient and average relative error are used to evaluate the validity of the model.

The results show that the maximum correlation coefficient of the seven estimated factors selected is 0.82; the estimation results of 14 test samples are of high accuracy, among which the determination coefficient R² = 0.924, and the average relative error is 6.608%.

Studies have shown that it is feasible and effective to estimate the content of soil organic matter by using grey correlation local linear regression model.

The paper succeeds in realizing both the soil organic matter hyperspectral grey relation estimating pattern based on the grey relational theory and the estimating pattern by using the local linear regression.

Intelligent and connected vehicle technology is in the ascendant. High-level autonomous driving places more stringent requirements on the accuracy and reliability of environmental perception. Existing research works on multitarget tracking based on multisensor fusion mostly focuses on the vehicle perspective, but limited by the principal defects of the vehicle sensor platform, it is difficult to comprehensively and accurately describe the surrounding environment information.

In this paper, a multitarget tracking method based on roadside multisensor fusion is proposed, including a multisensor fusion method based on measurement noise adaptive Kalman filtering, a global nearest neighbor data association method based on adaptive tracking gate, and a Track life cycle management method based on M/N logic rules.

Compared with fixed-size tracking gates, the adaptive tracking gates proposed in this paper can comprehensively improve the data association performance in the multitarget tracking process. Compared with single sensor measurement, the proposed method improves the position estimation accuracy by 13.5% and the velocity estimation accuracy by 22.2%. Compared with the control method, the proposed method improves the position estimation accuracy by 23.8% and the velocity estimation accuracy by 8.9%.

A multisensor fusion method with adaptive Kalman filtering of measurement noise is proposed to realize the adaptive adjustment of measurement noise. A global nearest neighbor data association method based on adaptive tracking gate is proposed to realize the adaptive adjustment of the tracking gate.

This paper aims to identify the main non-cost factors affecting accurate estimation of life cycle cost (LCC) in building projects.

Ten factors affecting LCC in building project cost estimates are identified through literature and interviews. A questionnaire survey is conducted to rank these factors in order of priority and provide the views of cost practitioners about the significance of these factors in the accurate estimation of LCC. The data from 138 construction building projects completed in UK were collected and analysed via multiple regression to discover the relationship between capital and LCCs and between non-cost factors and cost estimation at each stage of the life cycle (capital, operation, maintenance and LCC).

The results of analysis of existing LCC data of completing project and survey data from cost professionals are mostly consistent with many literature views and provide a reasonable description of the non-cost factors affecting the accuracy of estimates.

The value of this study is in the method used, which involves analysis of existing life data and survey data from cost professionals. The results provide a plausible description of the non-cost factors affecting the accuracy of estimates.

Estimating the sizes of query results and intermediate results is crucial to many aspects of query processing. All database systems rely on the use of cardinality estimates to choose the cheapest execution plan. In principle, the problem of cardinality estimation is more complicated in the Extensible Markup Language (XML) domain than the relational domain. The purpose of this paper is to present a novel framework for estimating the cardinality of XQuery expressions as well as their sub‐expressions. Additionally, this paper proposes a novel XQuery cardinality estimation benchmark. The main aim of this benchmark is to establish the basis of comparison between the different estimation approaches in the XQuery domain.

As a major innovation, the paper exploits the relational algebraic infrastructure to provide accurate estimation in the context of XML and XQuery domains. In the proposed framework, XQuery expressions are translated into an equivalent relational algebraic plans and then using a well defined set of inference rules and a set of special properties of the algebraic plan, this framework is able to provide high‐accurate estimation for XQuery expressions.

This paper is believed to be the first which provides a uniform framework to estimate the cardinality of more powerful XML querying capabilities using XQuery expressions as well as their sub‐expressions. It exploits the relational algebraic infrastructure to provide accurate estimation in the context of XML and XQuery domains. Moreover, the proposed framework can act as a meta‐model through its ability to incorporate different summarized XML structures and different histogram techniques which allows the model designers to achieve their targets by focusing their effort on designing or selecting the adequate techniques for them. In addition, this paper proposes benchmark for XQuery cardinality estimation systems. The proposed benchmark distinguishes itself from the other existing XML benchmarks in its focus on establishing the basis for comparing the different estimation approaches in the XML domain in terms of their accuracy of the estimations and their completeness in handling different XML querying features.

The current status of this proposed XQuery cardinality estimations framework does not support the estimation of the queries over the order information of the source XML documents and does not support non‐numeric predicates.

The experiments of this XQuery cardinality estimation system demonstrate its effectiveness and show high‐accurate estimation results. Utilizing the cardinality estimation properties during the SQL translation of XQuery expression results in an average improvement of 20 percent on the performance of their execution times.

This paper presents a novel framework for estimating the cardinality of XQuery expressions as well as its sub‐expressions. A novel XQuery cardinality estimation benchmark is introduced to establish the basis of comparison between the different estimation approaches in the XQuery domain.

Building cost is an important part of construction projects, and its correct estimation has important guiding significance for the follow-up decision-making of construction units.

This study focused on the application of back-propagation (BP) neural network in the estimation of building cost. First, the influencing factors of building cost were analyzed. Six factors were selected as input of the estimation model. Then, a BP neural network estimation model was established and trained by ten samples.

According to the experimental results, it was found that the estimation model converged at about 85 times; compared with radial basis function (RBF), the estimation accuracy of the model was higher, and the average error was 5.54 per cent, showing a good reliability in cost estimation.

The results of this study provide a reliable basis for investment decision-making in the construction industry and also contribute to the further application of BP neural network in cost estimation.