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During the COVID-19 pandemic, which has caused an unprecedented health and economic crisis around the globe, several countries, such as China and U.K., developed makeshift hospitals by converting public venues of other intended use (e.g., stadiums, convention centers, exhibition centers, gymnasiums, factories, and warehouses) to medical facilities, aiming to achieve in very little time a substantial upgrade of the health system's capacity. This change management capability is among the fundamental elements of infrastructure futureproofing, i.e., the process of making provision for future developments, needs or events that impact on particular infrastructure through its current planning, design, construction, or asset management processes. This article utilizes the limited available experience from these makeshift hospitals to shed light on the critical design parameters efficiently enabling the infrastructure to adapt to required changes in structure and/or operations. However, futureproofing should not be a standalone component but should be efficiently embedded in asset management practice. For this purpose, this article also proposes an appropriate associated delivery paradigm encompassing value management and building information modeling to allow the asset owners and designers to efficiently incorporate flexibility in their design planning. The structured approach of value management can be used to ensure that the project's characteristics are aligned to the public client's requirements for futureproofing while a BIM-based design management paradigm is particularly appropriate, as it allows for design changes to be shared, visualized, estimated, and resolved without the use of time-consuming paper transactions.

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Purpose -The purpose of this paper is to present an artificial neural network (ANN) model that predicts earthmoving trucks condition level using simple predictors; the model's performance is compared to the respective predictive accuracy of the statistical method of discriminant analysis (DA). Design/methodology/approach -An ANN-based predictive model is developed. The condition level predictors selected are the capacity, age, kilometers travelled and maintenance level. The relevant data set was provided by two Greek construction companies and includes the characteristics of 126 earthmoving trucks. Findings -Data processing identifies a particularly strong connection of kilometers travelled and maintenance level with the earthmoving trucks condition level. Moreover, the validation process reveals that the predictive efficiency of the proposed ANN model is very high. Similar findings emerge from the application of DA to the same data set using the same predictors. Originality/value -Earthmoving trucks' sound condition level prediction reduces downtime and its adverse impact on earthmoving duration and cost, while also enhancing the maintenance and replacement policies effectiveness. This research proves that a sound condition level prediction for earthmoving trucks is achievable through the utilization of easy to collect data and provides a comparative evaluation of the results of two widely applied predictive methods.

Accurate cost estimation in the preliminary stages of project development is critical for making informed planning decisions. However, such early estimates are typically restricted by limited information. In this paper, the widely recognised intelligence of Feed-Forward Artificial Neural Networks (FFANNs) is used to process actual data from 68 concrete road bridges and provide a surrogate model for the accurate estimation of the Bill-of-Quantities (BoQ). Specifically, twoFFANNs are trained to estimate the superstructure and piers concrete and steelbased on the construction method and the bridge dimensions. As the relevant metrics demonstrate, the FFANNs capture very well the complex interrelations in the dataset and produce highly accurate estimates. Furthermore, their generalization capability is superior to the capability ofrespectivelinear regression models. As the data used to train the FFANNs is normally available early in the project lifecycle, the proposed model enables early, yet accurate cost estimates to be obtained.

Purpose The purpose of this paper is to propose a new procurement strategy with the aim to achieve higher value for money (VFM) in public works delivery. Its main innovation lies in the possibility of optional submission of cost-efficient design variants by any interested contractor within the context of an open procedure. The final scope of works incorporates the variants approved, and all contractors are invited to submit a bid for the revised scope and budget. Design/methodology/approach This paper is a piece of applied research presenting the development of a new, cost-effective procurement strategy for public works, geared at the European Union (EU) legal framework. The strategy’s feature compilation has been based on comprehensive literature review while numerical data from a real world project were used to demonstrate its financial advantages. Findings The proposed strategy enables the delivery of the best value project at the lowest cost possible. This is achieved through ensuring high competition among competent contractors, improving the cost efficiency of technical solutions, discouraging future scope changes and establishing objectivity, fairness and transparency in the process of contract award. Practical implications The use of the proposed strategy results in public projects of enhanced VFM, reduced constructability issues and less scope changes during the construction stage. Originality/value The proposed strategy marks a new approach in procurement which enables the delivery of best VFM in public works. Therefore, it makes a valuable contribution towards the achievement of the overarching EU target for efficient public spending.