*Application of Artificial Neural Networks to Chemical and Process Engineering DOI: http://dx.doi.org/10.5772/intechopen.96641*

[50] Douglas JM. Conceptual Design of Chemical Processes. Vol. 1. New York: McGraw-Hill; 1988. 1110 p.

*Deep Learning Applications*

5475-5480.

American Chemical Society; 2001. p.

Zeaiter J. Dynamic optimization of dry reformer under catalyst sintering using neural networks. Energy Convers

Manag. 2018 Feb 1;157:146-156.

5;228:117539.

[43] Juybar M, Khanmohammadi Khorrami M, Bagheri Garmarudi A, Zandbaaf S. Determination of acidity in metal incorporated zeolites by infrared spectrometry using artificial neural network as chemometric approach. Spectrochim Acta - Part A Mol Biomol Spectrosc. 2020 Mar

[44] Schmal M. Heterogeneous Catalysis and its Industrial Applications. 1st ed.

[45] Assidjo E, Yao B, Kisselmina K, Amané D. Modeling of an industrial drying process by artificial neural networks. Brazilian J Chem Eng.

[46] Fernandes FAN, Lona LMF. Neural network applications in polymerization processes. Brazilian J Chem Eng.

[47] Alves RMB, Nascimento CAO. Gross errors detection of industrial data by neural network and cluster techniques. Brazilian J Chem Eng.

[48] Cai Q, Lee BCY, Ong SL, Hu J. Application of a Multiobjective Artificial Neural Network (ANN) in Industrial Reverse Osmosis Concentrate Treatment with a Fluidized Bed Fenton Process: Performance Prediction and Process Optimization. ACS ES&T

Switzerland: Springer; 2016.

2008;25(3):515-522.

2005;22(3):401-418.

2002;19(4):483-489.

Water. 2021;0-11.

[49] Lin M, Wu Y, Rohani S.

Simultaneous Measurement of Solution Concentration and Slurry Density by Raman Spectroscopy with Artificial Neural Network. Cryst Growth Des [Internet]. 2020 Mar 4 [cited 2021 Feb 8];20(3):1752-9. Available from: https:// dx.doi.org/10.1021/acs.cgd.9b01482

[36] Corma A, Serra JM, Argente E, Botti V, Valero S. Application of artificial neural networks to combinatorial catalysis: Modeling and predicting ODHE catalysts. ChemPhysChem. 2002;3(11):939-945.

[37] Valeh-E-Sheyda P, Yaripour F, Moradi G, Saber M. Application of artificial neural networks for estimation

of the reaction rate in methanol dehydration. Ind Eng Chem Res. 2010

[38] Günay ME, Yildirim R. Neural network Analysis of Selective CO Oxidation over Copper-Based Catalysts for Knowledge Extraction from Published Data in the Literature. Ind Eng Chem Res.

[39] Baumes L, Farrusseng D, Lengliz M, Mirodatos C. Using artificial neural networks to boost high-throughput discovery in heterogeneous catalysis. QSAR Comb Sci. 2004;23(9):767-778.

Conference on Sustainable Development of Energy, Water and Environment Systems - LA SDEWES. Rio de Janeiro;

May 19;49(10):4620-4626.

2011;50(22):12488-12500.

[40] Cavalcanti FM, Schmal M, Giudici R, Brito Alves RM. A Catalyst Selection Method for the Water-Gas Shift Reaction using Artificial Neural Networks. In: 1st Latin American

[41] Garona HA, Cavalcanti FM, Abreu TF, Schmal M, Brito Alves RM. Using Artificial Neural Networks for Fischer-Tropsch Synthesis to Lower-Olefins Production Optimization. In: 15th Conference on Sustainable Development of Energy, Water and Environment Systems - SDEWES.

Cologne; 2020. p. 1-15.

[42] Azzam M, Aramouni NAK, Ahmad MN, Awad M, Kwapinski W,

**182**

2018. p. 1-11.

[51] Yeomans H, Grossmann IE. A systematic modeling framework of superstructure optimization in process synthesis. Comput Chem Eng. 1999;23(6):709-731.

[52] Graciano JEA, Le Roux GAC. Improvements in surrogate models for process synthesis. Application to water network system design. Comput Chem Eng. 2013;59:197-210.

[53] Mencarelli L, Chen Q, Pagot A, Grossmann IE. A review on superstructure optimization approaches in process system engineering. Comput Chem Eng. 2020;136:106808.

[54] Biegler LT, Grossmann IE, Westerberg AW. Systematic Methods of Chemical Process Design. Upper Saddle River, NJ: Prentice Hall PTR; 1999. 808 p.

[55] Grossmann IE, Daichendt MM. New trends in optimizationbased approaches to process synthesis. Comput Chem Eng. 2003;20(6-7):665-683.

[56] Ryu J, Kong L, Pastore de Lima AE, Maravelias CT. A generalized superstructure-based framework for process synthesis. Comput Chem Eng. 2020;133:106653.

[57] Haykin S. Neural networks: a comprehensive foundation. Prentice Hall PTR; 1994.

[58] Henao CA, Maravelias CT. Surrogate-based process synthesis. Vol. 28, Computer Aided Chemical Engineering. Elsevier B.V.; 2010. 1129-1134 p.

[59] Savage T, Almeida-Trasvina HF, del Río-Chanona EA, Smith R, Zhang D. An adaptive data-driven modelling and

optimization framework for complex chemical process design. Comput Aided Chem Eng. 2020;48:73-78.

[60] Klemeš J, Friedler F, Bulatov I, Varbanov P. Sustainability in the Process Industry: Integration and Optimization. New York, Chicago, San Francisco, Lisbon, London, Madrid, Mexico City, Milan, New Delhi, San Juan, Seoul, Singapore, Sydney, Toronto: McGRAW-HILL; 2011. 385 p.

[61] Nascimento CAO, Giudici R, Guardani R. Neural network based approach for optimization of industrial chemical processes. Comput Chem Eng. 2000 Oct 1;24(9-10):2303-2314.

[62] Cai QQ, Lee BCY, Ong SL, Hu JY. Fluidized-bed Fenton technologies for recalcitrant industrial wastewater treatment–Recent advances, challenges and perspective. Vol. 190, Water Research. Elsevier Ltd; 2021. p. 116692.

[63] Alsaffar MA, Ghany MARA, Ali JM, Ayodele BV, Mustapa SI. Artificial Neural Network Modeling of Thermocatalytic Methane Decomposition for Hydrogen Production. Top Catal [Internet]. 2021 Jan 2 [cited 2021 Feb 8];1:3. Available from: https://doi. org/10.1007/s11244-020-01409-6

[64] Md Nor N, Che Hassan CR, Hussain MA. A review of datadriven fault detection and diagnosis methods: Applications in chemical process systems. Rev Chem Eng. 2020;36(4):513-553.

[65] Luo L, Xie L, Su H. Deep Learning with Tensor Factorization Layers for Sequential Fault Diagnosis and Industrial Process Monitoring. IEEE Access. 2020;8:105494-105506.

[66] Gao X, Yang F, Feng E. A process fault diagnosis method using multi-time scale dynamic feature extraction based on convolutional neural network. Can J Chem Eng. 2020;98(6):1280-1292.

[67] Peng C, Lu RW, Kang O, Kai W. Batch process fault detection for multistage broad learning system. Neural Networks. 2020;129:298-312.

**Chapter 10**

**Abstract**

construction sector

**1. Introduction**

**185**

Monitoring

Material Classification via

Machine Learning Techniques:

Construction Projects Progress

Nowadays, the construction industry is on a fast track to adopting digital processes under the Industrial Revolution (IR) 4.0. The desire to automate maximum construction processes with less human interference has led the industry and research community to inclined towards artificial intelligence. This chapter has been themed on automated construction monitoring practices by adopting material classification via machine learning (ML) techniques. The study has been conducted by following the structure review approach to gain an understanding of the applications of ML techniques for construction progress assessment. Data were collected from the Web of Science (WoS) and Scopus databases, concluding 14 relevant studies. The literature review depicted the support vector machine (SVM) and artificial neural network (ANN) techniques as more effective than other ML techniques for material classification. The last section of this chapter includes a pythonbased ANN model for material classification. This ANN model has been tested for construction items (brick, wood, concrete block, and asphalt) for training and prediction. Moreover, the predictive ANN model results have been shared for the

*Wesam Salah Alaloul and Abdul Hannan Qureshi*

readers, along with the resources and open-source web links.

**Keywords:** automated progress tracking, artificial intelligence, ANN,

The construction progress measuring practices are considered indispensable tools for effective project control [1]. Efficient and effective progress monitoring practices provide information regarding performance deviations to the execution plan and help the project management office (PMO) towards timely implementation of control actions to minimise the negative impacts [2]. Currently, instead of manual practices for construction progress assessment, the research community is fascinated by techniques such as photogrammetry, laser scanning, time-lapse photography, etc. Moreover, these strategies have also adopted 4D Building Information Modelling (BIM) as a framework to execute model-based progress tracking of construction projects [3]. In the last two decades, advancements in computer processes and digital camera technologies have allowed construction sector to

[68] Kamat S, Madhavan KP. Developing ANN based virtual/soft sensors for industrial problems. IFAC-PapersOnLine. 2016;49(1):100-105.

[69] Zhang Z, Wu Z, Rincon D, Christofides PD. Real-time optimization and control of nonlinear processes using machine learning. Mathematics. 2019;7(10):1-25.

[70] Wu Z, Tran A, Ren YM, Barnes CS, Chen S, Christofides PD. Model predictive control of phthalic anhydride synthesis in a fixed-bed catalytic reactor via machine learning modeling. Chem Eng Res Des. 2019;145:173-183.

[71] Chen S, Wu Z, Christofides PD. A cyber-secure control-detector architecture for nonlinear processes. AIChE J. 2020;66(5):1-18.

[72] Hernavs J, Ficko M, Berus L, Rudolf R, Klančnik S. Deep Learning in Industry 4 . 0 – Brief Overview. J Prod Eng. 2018;21(2):1-5.

[73] Duever TA. Data science in the chemical engineering curriculum. Processes. 2019;7(11).
