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Excavator productivity calculations embrace myriad variables, which in turn, can be modelled in several ways. A key productivity variable is operator competence (O _c ) because this can impact on so many of the other variables. Earlier research has studied excavator productivity, but little has attempted to simultaneously model productivity variables in relation to O _c . The purpose of this paper is to address the void in extant literature.

A numeric, theoretical analysis is undertaken using the Caterpillar® hydraulic excavator productivity model to estimate excavator production, given: first, variance in modifying factors based on derived maximum and minimum values; and second, variance resulting from linear calculations based on excavator operator competence.

Excavator productivity resulting from incremental variance of modifying factors in isolation is shown to be linear except, in the case of bucket payload. Simultaneous application of modifying variables results in a greater, curvilinear productivity trend; while it is demonstrated that quantification of key modifying factors can to a significant extent be related to operator competence.

Findings add to productivity literature generally and to that of plant and equipment more specifically. Results will help productivity estimation of excavation in a practical sense while informing subsequent design of an empirical academic research of this problem.

Originality relates principally to determining modifying factor ranges and their analysis of simultaneous effect on each other, especially, as influenced on assumptions of operator competence.

This paper aims to present a new non‐destructive method of brick wall dampness testing in real building structures. The electrical impedance tomography (EIT) method makes it possible to obtain a distribution of wall dampness. The paper aims to give basic information about the measuring system, including prototype equipment. The setup was used to determine the dampness of test brick walls on a specially built laboratory test rig. The paper seeks to compare test results obtained by the non‐destructive impedance tomography method with the results obtained by the conventional destructive dry‐weight method.

The paper adopts a tomography approach to control humidity inside of the brick walls. In case of brick walls other nondestructive methods fields, for example, the ultrasonic tomography is useless. On the other hand the most popular dry‐weight method is strictly forbidden for historical buildings. As a forward solver, functionally graded material boundary element method was used.

The paper proves that diffuse tomography could provide reliable results with respect of humidity content inside the brick walls. This method could provide 3D humidity distribution inside of the brick walls.

It is expected that the technique's impact will be limited to site inspection of building following floods or to evaluate older damp buildings.

The presented technique can eventually lead to much simpler, cheaper and more efficient evaluation of the moisture content in walls. This can revolutionize some procedures in civil engineering.

The application has commercial potential and could result in more cost‐effective repair of old buildings, which has an economic impact on society.

The authors propose application of the diffuse tomography for nondestructive investigation of brick walls. According to the authors' best knowledge this is a novel approach.

The study reported in this paper proposed the use of artificial neural networks (ANN) as viable alternative to regression for predicting the cost of building services elements at the early stage of design. The purpose of this paper is to develop, test and validate ANN models for predicting the costs of electrical services components.

The research is based on data mining of over 200 building projects in the office of a medium size electrical contractor. Of the over 200 projects examined, 71 usable data were found and used for the ANN modeling. Regression models were also explored using IBM Statistical Package for Social Sciences Statistics Software 21, for the purpose of comparison with the ANN models.

The findings show that the cost forecasting models based on ANN algorithm are more viable alternative to regression models for predicting the costs of light wiring, power wiring and cable pathways. The ANN prediction errors achieved are 6.4, 4.5 and 4.5 per cent for the three models developed whereas the regression models were insignificant. They did not fit any of the known regression distributions.

The validated ANN models were converted to a desktop application (user interface) package – “Intelligent Estimator.” The application is important because it can be used by construction professionals to reliably and quickly forecast the costs of power wiring, light wiring and cable pathways using building variables that are readily available or measurable during design stage, i.e. fully enclosed covered area, unenclosed covered area, internal perimeter length and number of floors.

Previous studies have concluded that the methods of estimating the budget for building structure and fabric work are inappropriate for use with mechanical and electrical services. Thus, this study is unique because it applied the ANN modeling technique, for the first time, to cost modeling of electrical services components for building using real world data. The analysis shows that ANN is a better alternative to regression models for predicting cost of services elements because the relationship between cost and the cost drivers are non-linear and distribution types are unknown.

This paper presents a new approach of productivity estimation of cutter suction dredger operation through data mining and learning from real-time big data.

The paper used big data, data mining and machine learning techniques to extract features of cutter suction dredgers (CSD) for predicting its productivity. ElasticNet-SVR (Elastic Net-Support Vector Machine) method is used to filter the original monitoring data. Along with the actual working conditions of CSD, 15 features were selected. Then, a box plot was used to clean the corresponding data by filtering out outliers. Finally, four algorithms, namely SVR (Support Vector Regression), XGBoost (Extreme Gradient Boosting), LSTM (Long-Short Term Memory Network) and BP (Back Propagation) Neural Network, were used for modeling and testing.

The paper provided a comprehensive forecasting framework for productivity estimation including feature selection, data processing and model evaluation. The optimal coefficient of determination (R²) of four algorithms were all above 80.0%, indicating that the features selected were representative. Finally, the BP neural network model coupled with the SVR model was selected as the final model.

Machine-learning algorithm incorporating domain expert judgments was used to select predictive features. The final optimal coefficient of determination (R²) of the coupled model of BP neural network and SVR is 87.6%, indicating that the method proposed in this paper is effective for CSD productivity estimation.

The study investigated the perceived causes of structural failure of public buildings, frequency of stability checks, stability checking procedures, measures to enhance public building stability checks and the roles of facility managers in the Accra Metropolis of Ghana.

Following a comprehensive literature review, the study employed a structured questionnaire survey and gathered the opinions of sixty-seven facility managers on the facility management practices. Following statistical pretesting of the dataset for reliability, distribution and agreement among the responses, the study analysed the dataset using mean scoring and weighted analysis.

The analysis showed that external building inspectors rarely inspect stability checks of the studied public buildings in Accra. It is also found that both reactive and proactive stability checking protocols are implemented in public buildings in Accra, but inadequate knowledge of facility managers limits technical stability checks. The study further revealed that stability checks of public buildings can be enhanced through incorporating site and location conditions into the design early upfront, active engagement of facility managers in the design and construction of public buildings, adequate budgetary provisioning for planned maintenance of public buildings, and encouraging appropriate use of public buildings.

This paper, to the best of the authors' knowledge, represents the first attempt to comprehensively examine the causes of structural failure of public buildings, frequency of stability checks, stability checking procedures, measures to enhance public building stability checks and the roles of facility managers in Ghana, from the perspective facility management.

Tracked hydraulic excavators are versatile and ubiquitous items of off-highway plant and machinery that are utilised throughout the construction industry. Each year, a significant number of excavators overturn whilst conducting a lifting operation, causing damage to property, personnel injury or even fatality. The reasons for the overturn are myriad, including: operational or environmental conditions; machine operator acts or omissions; and/or inadequate site supervision. Furthermore, the safe working load (SWL) figure obtained from manufacturer guidance and utilised in lift plans is based upon undertaking a static load only. The purpose of this paper is to determine whether the SWL is still safe to be used in a lift plan when slewing a freely suspended (dynamic) load, and, if not, whether this may be a further contributory factor to overturn incidents.

Previous research has developed a number of machine stability test regimes but these were largely subjective, impractical to replicate and failed to accurately measure the “dynamic” horizontal centrifugal force resulting from slewing the load. This research contributes towards resolving the stability problem by critically evaluating existing governing standards and legislation, investigating case studies of excavator overturn and simulating the dynamic effects of an excavator when slewing a freely suspended load at high rotations per minute (rpm). To achieve this, both the static load and horizontal centrifugal force from slewing this load were calculated for six randomly selected cases of an excavator, with different arm geometry configurations.

The results from the six cases are presented and a worked example of one is detailed to demonstrate how the results were derived. The findings reveal that the SWL quoted on an excavator’s lift rating chart considerably underestimates the extra forces experienced by the machine when an additional dynamic load is added to the static load whilst lifting and slewing a freely suspended load.

This work presents the first attempt to accurately model excavator stability by taking consideration of the dynamic forces caused by slewing a freely suspended load and will lead to changes in the way that industry develops and manages lift plans. Future research proposes to vary the weight of load, arm geometry and rpm to predict machine stability characteristics under various operational conditions, and exploit these modelling data to populate pre-programmed sensor-based technology to monitor stability in real time and automatically restrict lift mode operations.

This paper describes the development and implementation of a modular school building design prototype to support “build back better” after the disaster. The purpose of this paper is to bridge the gap between the two standard practices of post-disaster reconstruction: the quickly temporary construction and the permanent solution with longer time to complete.

The modular school design prototype was developed based on three design criteria established to achieve a relatively quick construction with good quality as a post-disaster permanent solution. The prototype was implemented in Kerandangan Village, Lombok and evaluated to review its compliance with the design criteria.

Three design strategies were proposed to respond to the main design criteria: the use of modular units and components, the material durability and availability, and the “plug-and-play” configuration system. Through these strategies, the prototype demonstrated the ability to perform as a permanent solution to be implemented in a short time. The prototype evaluation suggests some possible improvement to ensure a more efficient process and further replicability.

The development of the modular design bridges the gap between temporary and permanent approach for post-disaster school reconstruction. The highlighted criteria and the proposed design strategies contribute to the “build back better” attempt by providing better learning experiences for children through a replicable modular design that could be flexibly adapted to various local contexts.

This study aims at proposing a hybrid model for early cost prediction of a construction project. Early cost prediction for a construction project is the basic approach to procure a project within a predefined budget. However, most of the projects routinely face the impact of cost overruns. Furthermore, conventional and manual cost computing techniques are hectic, time-consuming and error-prone. To deal with such challenges, soft computing techniques such as artificial neural networks (ANNs), fuzzy logic and genetic algorithms are applied in construction management. Each technique has its own constraints not only in terms of efficiency but also in terms of feasibility, practicability, reliability and environmental impacts. However, appropriate combination of the techniques improves the model owing to their inherent nature.

This paper proposes a hybrid model by combining machine learning (ML) techniques with ANN to accurately predict the cost of pile foundations. The parameters contributing toward the cost of pile foundations were collected from five different projects in India. Out of 180 collected data entries, 176 entries were finally used after data cleaning. About 70% of the final data were used for building the model and the remaining 30% were used for validation.

The proposed model is capable of predicting the pile foundation costs with an accuracy of 97.42%.

Although various cost estimation techniques are available, appropriate use and combination of various ML techniques aid in improving the prediction accuracy. The proposed model will be a value addition to cost estimation of pile foundations.

Advanced construction technologies (ACTs) are transforming infrastructure projects, yet there has been little research into and theorization of the process by which these innovations are diffused. The purpose of this paper is to address this paucity of research by exploring the problems of information asymmetries between vendors and customers in the ACT diffusion process. Specifically, the paper explores whether information asymmetries exist between vendors and customers in the ACT diffusion process and what forms they take.

A structured survey of 153 vendors and customers of advanced construction technologies was undertaken across three international ACT exhibitions in Australia.

By comparing the perspectives of both customers and vendors across 15 technology diffusion process variables using importance-performance analysis and principal component analysis, significant differences are found between vendors’ and customers’ perceptions of how effectively information flows in the ACT diffusion process. The results show that vendors are significantly more optimistic than customers about information asymmetries on a wide range of diffusion variables. They also highlight significant potential for information asymmetries to occur which can undermine the advanced technology diffusion process.

The results provide important new conceptual and practical insights into an under-researched area, which is of increasing importance to a major industry, which is being transformed by advanced technological developments.

Globally, road projects are notorious for riskiness, which often results in cost overruns. In developing countries, these risks are amplified by economic instabilities and institutional failures. Majority of road projects in these countries are awarded to notedly inept indigenous contractors. Currently, research on the relationship between risks and cost performance of road projects has predominantly focussed on the client’s perspective. Effects of risks on contractors’ cost performance (profit) are inadequately investigated in literature. The purpose of this paper is to determine the relationship between direct risks and cost performance of road projects by indigenous contractors of developing countries from the contractors’ perspective.

The multivariate structural equation modelling technique was used to analyse purposively obtained data from indigenous contractors that recently completed road projects in Nigeria.

It was observed that a significant positive relationship exists between the aggregate project risk, i.e. project risk index of cost (PRIC) and cost performance of the projects. Significant positive relationships were also found to exist between identified cost risk centres and PRIC and between risk factors and cost risk centres. The risk centre site environment and location contributes the most to PRIC.

Indigenous contractors of developing countries are to analyse the identified risk factors and centres prior to bidding for road projects and carefully manage them during project execution.

Future studies of risks in road project should aim to obtain project risk indices of costs for the projects.