## 1 Introduction  
One of the most commonly used building materials in civil and structural engineering is concrete. However, the use of conventional concrete as a basic building material is facing growing challenges. The expansion of the construction industry has created significant demand for lightweight, high-strength structures for high-rise buildings, long-span bridges, and offshore platforms. Lightweight aggregate concrete (LWAC) contains light aggregates (LWAs), such as expanded clay, shale, or slate [1], and is an important and widely used material in advanced architecture, with a low density as the most obvious characteristic that distinguishes it from conventional concrete. LWAC stands out as an extremely versatile building material due to the wide range of potentially available LWAs. Over the last two decades, there has been growing interest in reducing the density of concrete while maintaining as much strength as possible [2]. LWAC also has certain advantages compared with conventional concrete, such as low thermal conductivity, better fire resistance, and insulation against heat and sound. Compared with conventional concrete, LWAC can effectively reduce dead load (thus reducing the costs due to the quantity of structural steel needed and the foundation size [3]) and can improve the seismic capacity of buildings [4]. Improving the performance of concrete is crucial in order to enable sustainable development. The rheological performance of fresh concrete is characterised by diversity and complexity, and fresh concrete must have sufficient workability to ensure the quality of concrete constructions [5]. In addition, good compaction is critical to ensure that the material fills all the voids in the formworks. When fresh LWAC is placed in the desired area, it must be compacted to remove entrapped air and voids, and to consolidate it. However, over-vibration of LWAC promotes segregation, in which denser aggregates settle to the bottom while the lower-density LWA tends to float, which affects its compressive strength and durability [6]. Over-vibration has been found to reduce the compressive strength by up to 67% depending on the density and the LWA used [7]. There are several factors that affect the segregation of concrete, for example, the components of the material, the rheology of the mortar, the sizes of the particles, the density of the produced concrete, and the compaction procedure, which induces anisotropy in the casting direction and has a negative impact on the properties of the concrete [8; 9]. There are several different methods of quantifying the segregation degree of concrete, most of which are based on standard density measurements, ultrasonic velocity measurements or image analysis [6; 10; 11; 12; 13; 14]. In particular, the ultrasonic pulse velocity method is a non-destructive approach that has been successfully used to evaluate the quality of concrete. It can also be used to detect internal cracking and other defects as well as changes in the concrete. The use of machine learning techniques has become an important area of research for predictive analysis in engineering (see, e.g., [6; 15; 16; 17] and the references therein). The enormous variety of formulae used to create LWAC with specific qualities and the continuously increasing complexity of cementitious systems remain challenging problems. The ability of machine learning to tackle complex tasks autonomously means that it has transformative potential for research into concrete [18]. In the context of building and construction materials, several works have reported the use of artificial neural networks (ANNs) to predict the compressive strength of concrete. For example, in [6], a multilayer perceptron (MLP) network was employed to estimate the influence of the segregation in concrete on the compressive strength of LWAC using ultrasonic pulse velocity.