Early Bill-of-Quantities Estimation of Concrete Road Bridges: An Artificial Intelligence-Based Application

Abstract

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.

Assessment of Barriers and Strategies for the Enhancement of Off-Site Construction in India: An ISM Approach

“…OSC promotes design standardization, which is critical for the economy of construction and the accurate estimation of labour productivity [17][18][19]. It also allows for the development of reliable early cost predictions [20,21], facilitates construction and helps avoid unnecessary complexity and errors [22,23]. The use of OSC and of related design principles have been extensively discussed in relation to a variety of well-known sustainability-promoting practices such as value management, buildability, waste minimization and lean construction [6,8,24,25].…”

Concrete Construction Engineering Management in the Context of the Internet of Things

“…Mahakud et al's project deals with the reuse of concrete blocks dismantled from C&D waste in the form of recycled coarse aggregate (RCA), which is replacing natural coarse aggregate in concrete and is used in the construction industry [4]. In Dimitriou et al's paper, the widely recognized feedforward artificial neural network (FFANN) intelligence is used to process real-world data from 68 concrete highway bridges and provide an alternative model for accurate Bill of Quantity (BoQ) estimation [5]. However, the above research is only at the theoretical stage at this stage, and the practicality is not too strong.…”

“…In order to establish a clock relationship between two adjacent nodes, this chapter adopts a bidirectional mechanism to exchange time information. In the bidirectional information exchange model between node A and reference node R, since the time to complete the information exchange cycle is very short, it is assumed that the clock parameters remain unchanged during the information exchange cycle [5]. First, the clock model is represented by a reference clock and accumulated clock offset:…”

Impact of it growth on labour demand in the EU transport infrastructure development

“…Hence, the construction companies, when facing a decision to shift to the big data tools, have to make a choice whether to stay with limited capabilities and higher risks of human errors of existing employees, or to substitute existing employees with the big data analytics tools and to hire expensive, skilled big data experts, who would tailor the big data tools to the company's operations and later maintain them for proper operation. Dimitriou, Marinelli, and Fragkakis (2018) analyse accurate cost estimation solutions for preliminary stages of project development. Accurately estimated the cost of a large and complex project significantly increases chances to win the tenders.…”