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Traditional construction planning relies upon the critical path method and bar charts. Both of these methods suffer from visualization and timing issues that could be addressed by 4D technology specifically geared to meet the needs of the construction industry. The purpose of this paper is to propose a new construction planning approach based on simulation by using a game engine.

A 4D automatic simulation tool was developed and a case study was carried out. The proposed tool was used to simulate and optimize the plans for the installation of a temporary platform for piling in a civil construction project in Hong Kong. The tool simulated the result of the construction process with three variables: equipment, site layout and schedule. Through this, the construction team was able to repeatedly simulate a range of options.

The results indicate that the proposed approach can provide a user-friendly 4D simulation platform for the construction industry. The simulation can also identify the solution being sought by the construction team. The paper also identifies directions for further development of the 4D technology as an aid in construction planning and decision making.

The tests on the tool are limited to a single case study and further research is needed to test the use of game engines for construction planning in different construction projects to verify its effectiveness. Future research could also explore the use of alternative game engines and compare their performance and results.

The authors proposed the use of game engine to simulate the construction process based on resources, working space and construction schedule. The developed tool can be used by end-users without simulation experience.

The purpose of this paper is to identify and prioritize management practices that have the potential to improve labor productivity in multi-storey building construction projects.

The study adopted two-phase mixed-methods research design and 58 project managers, contract administrators and project coordinators were involved in the survey. During Phase I, qualitative data were collected from 19 experts using interviews and the management practices that could enhance labor productivity in multi-storey building construction projects were identified. In Phase II, quantitative data were collected from 39 contractors involved in the delivery of multi-storey building projects by using questionnaires. The data were analyzed to prioritize the practices identified in Phase I.

Well-defined scope of work, safety and health policy, safety and health plan, hazard analysis, long-lead materials identification, safe work method statement, and toolbox safety meetings are the top seven practices that have the potential to improve labor productivity in multi-storey building projects.

The research identifies the management practices that can be implemented to enhance labor productivity in multi-storey building construction projects in the context of Australia. Being the first study in the Australian context, the findings can be used as benchmark for international comparison.

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.

This study aims to propose a hybrid simulation approach for site layout and material laydown planning in construction projects considering both the project’s continuous and discrete state.

Efficient site layout planning (SLP) is a critical task at the early stages of the project to enhance constructability and reduce safety risks, construction duration and cost. In this paper, external and internal conditions affecting SLP gets identified. Then dynamic features of project conditions and project operations are analyzed by using a hybrid simulation approach combining continuous simulation (CS) and discrete event simulation (DES).

An efficient site layout plan regarding the project conditions results in cost efficiency. Instead of using DES or CS alone, this paper uses a hybrid simulation approach. Such a hybrid method leads to more accurate results that enable construction managers to make better decisions, such as material management variables. The proposed approach is implemented in a real construction project (i.e. earthmoving operation) to evaluate the hybrid simulation approach’s performance.

The proposed approach is implemented in a real construction project (i.e. earthmoving operation) to evaluate the performance of the hybrid simulation approach.

Although DES is used widely in construction simulation, it involves some limitations or inefficiencies. On the other hand, modeling resource interactions and capturing the construction project’s holistic nature with CS or system dynamics face some challenges. This study uses a hybrid DES and CS approach to enhance commercial construction projects’ SLP.

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.