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Purpose Current facility management (FM) practices are inefficient and ineffective, partially because of missing information and communication issues. Information and communications technologies (ICT) are asserted to provide a promising solution for managing and operating facilities. However, the impact of ICT applications on current FM practices needs to be validated and the perception of FM professionals on ICT-based FM needs to be understood. Therefore, this paper aims to investigate the impacts and the perception of ICT application on FM practice and further develop an ICT-based integrated framework for smart FM practices. Design/methodology/approach To achieve the objective, the research starts with reviewing several promising ICT for FM, including building information modeling, geographic information systems, unmanned aerial vehicle and augmented reality. On this basis, a conceptional framework was synthesized in consideration of the benefits of each technology. A survey questionnaire to FM professionals was conducted to evaluate the proposed framework and identify the challenges of adopting ICT in the FM industry. Furthermore, return on investment and strength, weakness, opportunities and threats analysis have been used in this paper as evaluation methods for ICT industry adoption. Findings The survey results are validated by FM professionals for the future engagement of the integrated ICT applications. Also, the proposed framework can assist the decision-makers to have comprehensive information about facilities and systematize the communication among stakeholders. Originality/value This research provides an integrated framework for smart FM to improve decision-making, capitalizing on the ICT applications. Apart from this, the study sheds light on future research endeavors for other ICT applications.

PurposeHeavy 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.Design/methodology/approachBased 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.FindingsThe 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.Originality/valueThe proposed novel model is designed as a supportive tool for construction project managers for equipment selling, purchasing, overhauling, repairing, disposing and replacing decisions.

Purpose Asset inventory is an essential part of any building asset management system and is needed by such functions as condition assessment and deterioration prediction. Previous studies in asset management systems have suggested the use of one of the many standard construction classification systems, such as UniFormat or MasterFormat, in achieving the goals of asset management. However, each classification system has its unique features, and it has been developed for different purposes and may not necessarily be directly adaptable to asset management. A proper classification system is thus needed to achieve the goals of building asset management effectively. Such a system must take into consideration the objectives and functions of asset management. Therefore, the purpose of this paper is to establish a unified work breakdown structure (WBS)-based framework for building asset inventory. Design/methodology/approach The WBS-based framework aims to cover the entire lifecycle of an asset so as to provide the unified classification system for asset inventory. The proposed framework is developed based on appropriate building standards. Also, comprehensive levels of details are included for space functions and locations for all assets in any type of building. Furthermore, this framework takes into consideration utilities in any kind of building project. As such, the WBS-based framework proposed in this research endeavor provides the basis for effective asset management. An educational building case study is presented and discussed to demonstrate the effectiveness of the proposed framework for asset management. Findings The unified WBS-based framework for building asset management effectively classifies asset inventories and facilitates decision-making in asset management during the lifecycle of an asset. Originality/value This research synthesizes a unified WBS-based framework for building asset management, which allows for a more effective lifecycle building asset management.

The construction work environment remains one of the most hazardous among all industries. Construction injuries directly impact the workers and the work itself, including personal suffering, construction delays, productivity losses, higher insurance premiums, and possible liability suits for all parties involved in the project. The costs resulting from personal injuries, combined with the associated financial impact resulting from schedule disruptions, insurance hikes, and workers’ compensation, can impact a project’s profitability. Many of these impacts can be minimized or avoided through the continuous assessment and improvement of safety policies and practices. This paper aims to propose a new safety assessment methodology that equips insurance companies and construction managers with an optimal mechanism for evaluating the safety performance of construction companies. The proposed model consists of 20 evaluation criteria that are used to establish the efficiency benchmarks and provide comparison feedback for improving the company’s safety plans and procedures. These criteria are determined based on leading and lagging safety performance indicators. The data envelopment analysis (DEA) technique is used as the underlying model to assess the relative efficiency of safety practices objectively. Two illustration case studies are provided to demonstrate the dual effectiveness of the DEA model. The presented research contributes to the body of knowledge by formalizing a robust, effective, and consistent safety performance assessment. The model equips the company with the ability to track both the progression and the retrogression over time and provides feedback on ineffective practices that need more attention. Simultaneously, the model gives them more detailed safety performance information that can replace the current experience modification rating (EMR) approach. It provides insurance companies with an objective and robust evaluation model for selecting optimum rates for their clients. In addition, the data comparison utility offered by the DEA model and its criteria can be helpful for insurance companies to provide effective advice to their clients on which safety aspects to improve in their future strategies.

The specialty electrical and mechanical contracting sectors provide crucial services and perform functions that are vital to the products delivered by the construction industry. The main purpose of this study is to investigate the causes of fatal and nonfatal injuries in these specialty construction sectors over time as well as their effects on the level of safety performance in the industry. Accordingly, the most prevalent causes of fatal and nonfatal incidents in the mechanical and electrical sectors are investigated and presented as a longitudinal study from 2005 to 2015. The trends in occupational injuries in these sectors over this period of time are also compared to the trends reported in previous studies. The results from this study show that the direct causes of fatal and nonfatal injuries in the electrical and mechanical fatal and nonfatal injuries differ from those found in the construction industry in general. However, the electrical and mechanical construction industry trends identified in this study are similar to previously reported trends. The similarities between the current findings and those of previous studies highlight real shortcomings in the safety management approaches within the construction industry. Based on the findings of this study, a learning investigation system has been proposed to improve safety performance among electrical and mechanical specialty contractors.

Construction is one of the most hazardous industries worldwide. Implementing safety regulations is the responsibility of all parties involved in a construction project and must be performed systematically and synergistically to maximize safety performance and reduce accidents. This study aims to examine the level of safety compliance of construction personnel (i.e., top management, frontline supervisors, safety coordinators/managers, and workers) to gain insight into the top safety measures that lead to no major or frequent accidents and to predict the likelihood of having a construction site free of major or frequent accidents. To achieve the objectives, five safety measures subsets were collected and modeled using six combinations of five different Bayesian networks (BNs). The performance of these model classifiers was compared in terms of accuracy, sensitivity, specificity, recall, precision, F-measure, and area under the receiver operating characteristic curve. Then, the best model for each data subset was adopted. The inference was then performed to identify the probability of the commitment to safety measures to reduce major or frequent accidents and recommend enhancement regulations and practices. While the context in this paper is the Jordanian construction industry, the novelty of the work lies in the BN modeling methodology and recommendations that any country can adopt for evaluating the safety performance of its construction industry. This research endeavor is, therefore, a significant step toward providing knowledge about the top safety measures associated with reducing accidents and establishing efficiency comparison benchmarks for improving safety performance.

Public transportation services often face challenges in middle- and low-income nations from both public acceptance and economic constraints. Jordan is classified as a middle-income country with a population of over 10 million, 4 million of whom live in Amman, the capital. The development and operation of a bus rapid transit (BRT) system in Amman city was recently proposed. The BRT project is anticipated to offer a solution to the city’s escalating congestion problem. This study’s objective is to conduct a “before” analysis to identify the variables that affect the willingness of people to use the Amman BRT system. The socioeconomic characteristics and travel habits of individuals were used to model the willingness to use BRT. An online survey was distributed to Amman residents and 238 valid responses were returned. Two popular techniques were utilized: binary logistic regression and Bayesian networks. Ten models were developed: one binary logistic model and nine Bayesian network models. The results of these models were compared based on accuracy, sensitivity, specificity, area under the Receiver Operating Characteristic (ROC) curve, complexity, and number of selected variables. It was found that Bayesian networks were more effective in modeling willingness to use BRT. Willingness to use BRT was shown to be higher among households without cars, youths, females, and university students, and if there were fewer transfers along the route. It became clear that introducing a new public transportation system is well appreciated, particularly in areas with low income, insufficient existing public transportation services, and where driving a car is the norm.