Data-driven approaches are gaining popularity in structural health monitoring (SHM) due to recent technological advances in sensors, high-speed Internet and cloud computing. Since Machine learning (ML), particularly in SHM, was introduced in civil engineering, this modern and promising method has drawn significant research attention. SHM’s main goal is to develop different data processing methodologies and generate results related to the different levels of damage recognition process. SHM implements a technique for damage detection and classification, including data from a system collected under different structural states using a piezoelectric sensor network using guided waves, hierarchical non-linear primary component analysis and machine learning. The primary objective of this paper is to analyse the current SHM literature using evolving ML-based methods and to provide readers with an overview of various SHM applications. The technique and implementation of vibration-based, vision-based surveillance, along with some recent SHM developments are discussed.

The construction industry is increasingly focused on sustainability, with a particular emphasis on reducing the environmental impact of cement production. One approach to this problem is to use recycled materials and explore eco-friendly raw materials, such as alumino-silicate by-products like fly ash, which can be used as raw materials for geopolymer concrete. To enhance the ductility, failure mode, and toughness of the geopolymer, researchers have added crumb rubber processed from scrap tires as partial replacement to fine aggregate of the geopolymer. Therefore, this study aims to develop rubberized geopolymer concrete (RGC) by partially replacing the fine aggregate with crumb rubber (CR). To optimize the mechanical properties of RGC, response surface methodology (RSM) has been used to develop 13 mixes with different levels and proportions of CR (10–30% partial replacement of fine aggregate by volume) and sodium hydroxide molarity (10–14 M) as input variables. The results showed that the strength properties increased as the molarity of NaOH increased, while the opposite trend was observed with CR. The maximum values for compressive strength, flexural strength, and uniaxial tensile strength were found to be 25 MPa, 3.1 MPa, and 0.41 MPa, respectively. Response surface models of the mechanical strengths, which were validated using ANOVA with high R² values of 72–99%, have been developed. It has been found that using 10% CR with 14 M sodium hydroxide resulting in the best mechanical properties for RGC, which was validated with experimental tests. The result of the multi-objective optimization indicated that the optimum addition level for NaOH is 14 M, and the fine aggregate replacement level with CR is 10% in order to achieve a rubberized geopolymer suitable for structural applications.

Blockchain is a way of passing data (such as records, events, or transactions) from one party to another in a very secure way. It is an electronic record of information that requires digital security. All data stored in the blockchain is immutable; once a piece of data enters into a blockchain, it is practically impossible to alter its value. The Blockchain has changed the model from a centralized way of traditional business to the decentralized model of the blockchain, that means there it can run without any central authority. It works on Peer to Peer Model rather than Peer-Mediator-Peer. The Blockchains makes the process easier, fastest, and trustworthy to deal with businesses as it follows Peer to Peer nature Blockchain. It has become the most used business model in different industries, such as construction industries, as it is the safest, fastest, transparent, and it also is more comfortable to implement. The critical feature of the blockchain that makes it today’s most potential technologies are: decentralized, self-control, peer- to- peer relationship, fixed record and time stamping. Thus, this chapter focuses on the pivotal role and application of BlockChain technology on the digitization of land records of the Indian Scenario.