Secondary raw materials (SRMs) tend to be a valuable replacement for finite virgin materials especially since construction works (i.e., building and civil engineering work such as road construction) require vast quantities of raw materials. Using SRM originating from recycling a broad range of inorganic waste materials (e.g., mining waste, different industrial wastes, construction, and demolition waste) has been recognized as a promising, generally more cost-efficient, and environmentally friendly alternative to the exploitation of natural resources. Despite the benefits of using SRM, several challenges need to be addressed before using SRM even more. One of them is the long-term durability and little-known response of construction works built using such alternative materials. In this paper, we present the activities to establish a fully functioning digital twin (DT) of a road constructed using SRM. The first part of the paper is devoted to the theoretical justification of efforts and ways of establishing the monitoring systems, followed by a DT case study where an integrated data environment synthesizing a Building Information Model and monitored data is presented. Although the paper builds upon a small scale, the case study is methodologically designed to allow parallels to be drawn with much larger construction projects.

Uneven road surfaces are the primary source of excitation in the dynamic interaction between a bridge and a vehicle and can lead to errors in bridge weigh-in-motion (B-WIM) systems. In order to correctly reproduce this interaction in a numerical model of a bridge, it is essential to know the magnitude and location of the various roadway irregularities. This paper presents a methodology for measuring the 3D road surface using static terrestrial laser scanning and a numerical model for simulating vehicle passage over a bridge with a measured road surface. This model allows the evaluation of strain responses in the time domain at any bridge location considering different parameters such as vehicle type, lateral position and speed, road surface unevenness, bridge type, etc. Since the time domain strains are crucial for B-WIM algorithms, the proposed approach facilitates the analysis of the different factors affecting the B-WIM results. The first validation of the proposed methodology was carried out on a real bridge, where extensive measurements were performed using different sensors, including measurements of the road surface, the response of the bridge when crossed by a test vehicle and the dynamic properties of the bridge and vehicle. The comparison between the simulated and measured bridge response marks a promising step towards investigating the influence of unevenness on the results of B-WIM.

With the development of computer technology, artificial neural networks are becoming increasingly useful in the field of engineering geology and geotechnics. With artificial neural networks, the geomechanical properties of rocks or their behaviour could be predicted under different stress conditions. Slope failures or underground excavations in rocks mostly occurred through joints, which are essential for the stability of geotechnical structures. This is why the peak shear strength of a rock joint is the most important parameter for a rock mass stability. Testing of the shear characteristics of joints is often time consuming and suitable specimens for testing are difficult to obtain during the research phase. The roughness of the joint surface, tensile strength and vertical load have a great influence on the peak shear strength of the rock joint. In the presented paper, the surface roughness of joints was measured with a photogrammetric scanner, and the peak shear strength was determined by the Robertson direct shear test. Based on six input characteristics of the rock joints, the artificial neural network, using a backpropagation learning algorithm, successfully learned to predict the peak shear strength of the rock joint. The trained artificial neural network predicted the peak shear strength for similar lithological and geological conditions with average estimation error of 6 %. The results of the calculation with artificial neural networks were compared with the Grasselli experimental model, which showed a higher error in comparison with the artificial neural network model.