## 2 Related Work  
Artificial intelligence (AI) technologies and machine learning (ML) algorithms have been increasingly utilized to solve hard problems that inherit nondeterminism and randomness in engineering practice of a variety of fields including complex system control [19; 20], object detection [21], communication networks [22], oil pipeline monitoring [23] and leak detection [24], industrial control system protection [25], waste material recycling [26], etc. In recent years, more and more AI/ML practitioners collaborate with experts in different industrial domains to apply data-driven methods to help solve domain-specific problems and achieve the \"Smart Manufacturing\" and \"Industry 4.0\" goals of integrating computing machine intelligence into their respective manufacture production processes [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15]. Numerous surveys about the applications of these AI/ML methods to different manufacturing industries have been published. For example, [PERSON] et al. in [27] gave a comprehensive review about the adoptions of ML methods in concrete science that have a positive impact. [PERSON] et al. reviewed reinforcement learning (RL) methods and surveyed their applications in industrial process control [28]. [PERSON] et al. in [29] sketched out a generic framework for using learning-based technologies to solve problems in complex industrial processes. [PERSON] et al. focused on the strategies of tuning predictive controls model and conducted a comprehensive survey covering both analytical methods such as ordinary differential equations and learning-based methods such as neural networks [30]. Here, we particularly focus on the applications of optimization techniques in the concrete and cement industries and present an up-to-date survey about the adoptions of machine learning (ML) and other advanced control methods in various stages and processes of the concrete and cement productions, spanning from energy efficiency [13; 31], concrete porosity [12], free lime content [32; 33; 34], ball milling [6; 7], to advanced process control and optimization [30; 35]. [PERSON] et al. back in 2006 [36] already adopted data-driven approaches to develop soft sensors for systematically monitoring the cement quality. Later, in 2012, [PERSON] et al. adopted feedforward neural network with a radial basis function (RBF) activation to perform prediction of free calcium oxide content in clinker and obtained results much better than methods such as linear/non-linear regression [33]; and in 2016, [PERSON] et al. in [37] developed soft sensing models based on feed-forward artificial neural network and fuzzy inference to monitor cluster quality online and observed acceptable results. In more recent years, [PERSON] et al. applied various machine learning methods to predict energy consumption of the grinding process in cement mills and reported accuracy improvement of nearly one order of magnitude above their baseline [31]. [PERSON] et al. used artificial neural networks and genetic algorithms to optimize the cost of electricity in cement plants with electricity costs regulated country by country and factors affecting electricity price taken into account [14]. [PERSON] applied regression tree ensembles to predict the porosity (a durability indicator of concrete) of concrete containing supplementary cementitious materials and further empirically identified important dominating features that impact the concrete porosity, i.e., curing days, water/binder ratio, and aggregate content [12]. [PERSON] et al. conducted similar work using support vector machine (SVM) ensembles in [32]. [PERSON] et al. tried to solve the same free lime content modeling problem using a particle swarm optimization (PSO) algorithm [34]. [PERSON] et al. developed soft sensors based on a backpropagation neural network model to perform online prediction of the cement fineness inside of a ball mill [7]. [PERSON] et al.