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Downtime (DT) caused by non‐availability of equipment and equipment breakdown has non‐trivial impact on the performance of construction projects. Earlier research has often addressed this fact, but it has rarely explained the causes and consequences of DT – especially in the context of developing countries. This paper presents a DT model to address this issue. Using this model, the generic factors and processes related to DT are identified, and the impact of DT is quantified. By applying the model framework to nine road projects in Nepal, the impact of DT is explored in terms of its duration and cost. The research findings highlight how various factors and processes interact with each other to create DT, and mitigate or exacerbate its impact on project performance. It is suggested that construction companies need to adopt proactive equipment management and maintenance programs to minimize the impact of DT.

This study proposes a framework for Earthwork Ontology (EW-Onto) to support and enhance data exchange in the project and the efficient decision-making in the planning and execution phases.

The development of EW-Onto started from defining the concepts and building taxonomies for earthwork operations and equipment following the METHONTOLOGY approach. In addition, several rules have been extracted from safety codes and implemented as SWRL rules. The ontology has been implemented using Protégé. The consistency of EW-Onto has been checked and it has been evaluated using a survey.

The assessment of EW-Onto by experts indicates an adequate level of consensus with the framework, as an initial step for explicit knowledge exchanges within the earthwork domain.

The use of an ontology within the earthwork domain can help: (1) link and identify the relationships between concepts, define earthwork semantics, and classify knowledge in a hierarchical way accepted by experts and end-users; (2) facilitate the management of earthwork operations and simplify information exchange and interoperability between currently fragmented systems; and (3) increase the stakeholders' knowledge of earthwork operations through the provision of the information, which is structured in the context of robust knowledge.

This paper proposes a framework for Earthwork Ontology (EW-Onto) to support and enhance data exchange in the project and the efficient decision-making in the planning and execution phases. EW-Onto represents the semantic values of the entities and the relationships, which are identified and formalized based on the basic definitions available in the literature and outlined by experts.

Contractors’ personnel play a prominent role in enhancing the constructability of facilities design, construction and assessment. Looks at the constructability concepts identified by the Construction Industry Institute at Austin, Texas (CII) and represents and describes the constructability concepts in relation to contractors’ personnel. Discusses the role of each of them in enhancing constructability of facilities projects. Concludes that a set of obligations are the answer to the question of “How can the contractor’s personnel enhance the project constructability?”.

The equipment resale price plays an important role in calculating the optimum time for equipment replacement. Some of the existing models that predict the equipment resale price do not take many of the influencing factors on the resale price into account. Other models consider more factors that influence equipment resale price, but they still with low accuracy because of the modeling techniques that were used. An easy tool is required to help in forecasting the resale price and support efficient decisions for equipment replacement. This research presents a machine learning (ML) computer model helping in forecasting accurately the equipment resale price.

A measuring method for the influencing factors that have impacts on the equipment resale price was determined. The values of those factors were measured for 1,700 pieces of equipment and their corresponding resale price. The data were used to develop a ML model that covers three types of equipment (loaders, excavators and bulldozers). The methodology used to develop the model applied three ML algorithms: the random forest regressor, extra trees regressor and decision tree regressor, to find an accurate model for the equipment resale price. The three algorithms were verified and tested with data of 340 pieces of equipment.

Using a large number of data to train the ML model resulted in a high-accuracy predicting model. The accuracy of the extra trees regressor algorithm was the highest among the three used algorithms to develop the ML model. The accuracy of the model is 98%. A computer interface is designed to make the use of the model easier.

The proposed model is accurate and makes it easy to predict the equipment resale price. The predicted resale price can be used to calculate equipment elements that are essential for developing a dependable equipment replacement plan. The proposed model was developed based on the most influencing factors on the equipment resale price and evaluation of those factors was done using reliable methods. The technique used to develop the model is the ML that proved its accuracy in modeling. The accuracy of the model, which is 98%, enhances the value of the model.

Heavy equipment residual value forecasting is dynamic as it relies on the age, type, brand and model of the equipment, ranking condition, place of sale, operating hours and other macroeconomic gauges. The main objective of this study is to predict the residual value of the main types of heavy construction equipment. The residual value of heavy construction equipment is predicted via deep learning (DL) and machine learning (ML) approaches.

Based on deep and machine learning regression network integrated with data mining, random forest (RF), decision tree (DT), deep neural network (DNN) and linear regression (LR)-based modeling decision support models are developed. This research aims to forecast the residual value for different types of heavy construction equipment. A comprehensive investigation of publicly accessible auction data related to various types and categories of construction equipment was utilized to generate the model's training and testing datasets. In total, four performance metrics (i.e. the mean absolute error (MAE), mean squared error (MSE), the mean absolute percentage error (MAPE) and coefficient of determination (R2)) were used to measure and compare the developed algorithms' accuracy.

The developed algorithm's efficiency has been demonstrated by comparing the deep and machine learning predictions with real residual value. The accuracy of the results obtained by different proposed modeling techniques was comparable based on the performance evaluation metrics. DT shows the highest accuracy of 0.9111 versus RF with an accuracy of 0.8123, followed by DNN with an accuracy of 0.7755 and the linear regression with an accuracy of 0.5967.

The proposed novel model is designed as a supportive tool for construction project managers for equipment selling, purchasing, overhauling, repairing, disposing and replacing decisions.

Utilization of off‐highway vehicles forms an essential part of UK industry's efforts to augment the productivity of plant operations and reduce production costs. However, uninterrupted utilization of plant and equipment is requisite to reaping the maximum benefit of mechanization; one particular problem being plant breakdown duration and its impact upon process productivity. Predicting the duration of plant downtime would enable plant managers to develop suitable contingency plans to reduce the impact of downtime. This paper presents a stochastic mathematical modelling methodology (more specifically, probability density function of random numbers) which predicts the probable magnitude of ‘the next’ breakdown, in terms of duration for tracked hydraulic excavators. A random sample of 33 machines was obtained from opencast mining contractors, containing 1070 observations of machine breakdown duration. Utilization of the random numbers technique will engender improved maintenance practice by providing a practical methodology for planning, scheduling and controlling future plant resource requirements. The paper concludes with direction for future research which aims to: extend the model's application to cover other industrial settings and plant items; to predict the time at which breakdown will occur (vis‐à‐vis the duration of breakdown); and apply the random numbers modelling to individual machine compartments.

The criticality of mechanical plant to construction activity is well accepted within the literature; however, the supply chain mechanisms by which that demand is satisfied, are much less documented or understood. The purpose of this paper is to address this theoretical gap by: describing Construction Plant Supply Chain (CPSC) evolvement; identifying with present sector difficulties; discussing solutions to those difficulties; and considering the role of innovation within CPSC (historically and for the future).

A mixed‐method research, i.e. qualitative and preliminary, including literature review, case study inquiry of an established multi‐purpose CPSC player, and open question survey of a limited sample of CPSC stakeholders has been employed in this study. Inductive data analysis via textual interrogation is undertaken.

In reaction to market forces and business challenges, CPSC evolution demonstrated innovative change from former contractor‐held plant fleets to predominantly private sector “external” supply chains. Of late, CPSC challenges have intensified, given its intrinsic relationship to a depressed UK (and global) economy, dependency on capital investment, and the need for sustained demand. Suggestions to encounter present challenges have been made and a difficult medium‐term future signified.

As a preliminary study, generalisation of findings should be viewed in a limited context; however, given the dearth of research in this subject, the findings make novel contribution to the CPSC literature while signposting fertile avenues for future and more comprehensive research.

No previous research in this specific field has been identified.

Using statistics obtained from the Health and Safety Executive, compares accident rates occurring within the UK construction industry to the accident rates occurring within other industries; then assesses and discusses these. Results reveal that the construction industry is arguably the most hazardous industry and has consistently recorded a poor accident record. Off‐highway plant and equipment is a considerable contributor to the industry’s infamous record. Then assesses accidents relating to individual plant items and discusses the underlying reasons for such accidents. Part of the problem stems from poor mechanical design but in the majority of cases the operator is at fault. Training programmes and initiatives have previously attempted to address this problem but regrettably the Provision and Use of Work Equipment regulations do not enforce mandatory training and certification. Provides a potential solution to this problem through the use of psychometric test development. Hypothesizes such an approach as representing a useful technique for both improving the skills and competence of existing operatives, and aiding the selection process when hiring new operatives.

The momentous contribution of innovative technologies has made a significant impact in several sectors globally. However, the construction industry is undoubtedly lagging when it comes to technology usage. Thus, this study aims to explore the various emerging technologies in the construction industry while noticing stakeholders’ challenges and strategies in its use.

The study used a pragmatism research philosophy together with a quantitative research strategy in determining emerging technologies in the construction industry while noticing stakeholder challenges and strategies. Data were obtained from a total of 80 construction stakeholders through a structured questionnaire survey. The analysis was done with descriptive statistics using mean score ranking and a one-sample t-test.

Each emerging technology challenge was analysed and compared to see how pressing the challenges were as well as the aligned strategies. A key indication of this study is that the familiarity of the various emerging technologies was based on how many occasions one had an encounter with the technology.

The discussion’s findings contribute to a better knowledge to construction stakeholders on the challenges and strategies for rising technology adoption and implementation competencies.

The study reckoned stakeholders’ challenges on the emerging technologies in the construction industry context and recommended strategies to balloon the adoption of these emerging technologies in a developing country setting.