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Currently, owners find it difficult to manage their assets throughout their project life cycle. The fact that asset information models (AIMs) are mandatory as deliverables for building information modeling-driven projects makes it a key requirement for the client to understand in detail those factors affecting asset operation. Hence, because the Kingdom of Saudi Arabia is the most significant market in the Middle East, this study aims to investigate those factors where blockchain and AIMs could impact the asset management (AM) life cycle.

Researchers used a hypothesis-based approach over a systematic literature review and a workshop (descriptive statistics) to understand the current challenges in AM. Later, a second workshop was run to understand the impact factor analysis affecting the operation of the asset life cycle by using asset information modeling and blockchain technology over a multiquantitative method.

Results found that factors affecting the operation of assets could be the improvement of trust and stakeholder’s influences; the availability of handover process products’ accurate data; manufacturers providing detailed product models; increasing the speed of preparing holistic and integrated AM systems; improving collaboration between stakeholders; and returning clients’ investments faster.

Understanding the factors affecting AM life cycle based on the utilization of AIMs and blockchain then allows investors and their team members to work in a secure and collaborative environment that helps them to pre-identify certain risks and improve decision-making in a more effective way, as is required by ISO55000.

Buildings are major contributors to greenhouse gases (GHG) along the various stages of the building life cycle. A range of tools have been utilised for estimating building energy use and environmental impacts; these are time-consuming and require massive data that are not necessarily available during early design stages. Therefore, this study aimed to develop an Environmental Impacts Cost Assessment Model (EICAM) that quantifies both energy and environmental costs for residential buildings.

An Artificial Neural Network (ANN) was employed to develop the EICAM. The model consists of six input parameters, including wall type, roof type, glazing type, window to wall ratio (WWR), shading device and building orientation. In addition, the model calculates four measures: annual energy cost, operational carbon over 20 years, envelope embodied carbon and total carbon per square metre. The ANN architecture is 6:13:4:4, where the conjugate gradient algorithm was applied to train the model and minimise the mean squared error (MSE). Furthermore, regression analysis for the ANN prediction for each output was performed.

The MSE was minimised to 0.016 while training the model. Also, the correlation between each ANN output and the actual output was very strong, with an R² value for each output of almost 0.998. Moreover, validation was conducted for each output, with the error percentages calculated at 0.26%, 0.25%, 0.03% and 0.27% for the annual energy cost, operational carbon, envelope materials embodied carbon and total carbon per square metre, respectively. Accordingly, the EICAM contributes to enhancing design decision-making concerning energy consumption and carbon emissions in the early design stages.

This study provides theoretical implications to the domain of building environmental impact assessment through illustrating a systematic approach for developing an energy-based prediction model that generates four environmental-oriented outputs, namely energy cost, operational energy carbon, envelope embodied carbon, and total carbon. The model developed has practical implications for the architectural/engineering (A/E) industries by providing a useful tool to easily predict environmental impact costs during the early design phase. This would enable designers in Saudi Arabia to make effective design decisions that would increase sustainability in the building life cycle.

By providing a holistic predictive model entitled EICAM, this study endeavours to bridge the gap between energy costs and environmental impacts in a predictive model for Saudi residential units. The novelty of this model is that it is an alternative tool that quantifies both energy cost, as well as building’s environmental impact, in one model by using a machine learning approach. Besides, EICAM predicts its outcomes more quickly than conventional tools such as DesignBuilder and is reliable for predicting accurate environmental impact costs during early design stages.