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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.

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.

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.

This research synthesizes a unified WBS-based framework for building asset management, which allows for a more effective lifecycle building asset management.

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.

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.

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.

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.

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.