The Internet of Things is a comprehensive system of connected computing devices and sensors that provide extensive data sharing capability for any specific purpose. For the construction industry, the applications of the Internet of Things have been increasing over the past few years, and it is because technology can provide full support to construction projects in attaining significant efficiency. The most critical part of construction products where the internet of things can be adopted is safety management because hundreds of accidents happen every year that result in significant injuries to construction workers and even death in some cases. For small construction projects, the situation is much worse, as there are never enough resources to adopt the latest technology, such as the Internet of Things. This study is structured with the aim of identifying the critical implementation barriers of the internet of things that affect small construction projects in Malaysia specifically. A mixed methodology study design is followed in which, after identifying the implementation barriers of the internet of things from existing literature, they are filtered with expert opinion. A pilot survey was conducted on which exploratory factor analysis was applied to further identify the significant barriers relating to the Internet of Things in small construction projects in Malaysia. A main survey was conducted afterwards, on which the structural equation modelling was done to develop the model involving the final 16 barriers divided into 5 formative constructs. The most critical barriers are found to be related to databases and technology, while the least impact is created by management barriers. Positive theoretical and managerial implications are indicated for future researchers and construction workers, respectively, by which they can improve the implementation of internet of things in small construction projects in Malaysia.

To achieve a targeted production level in mining industries, all machine systems and their subsystems must perform efficiently and be reliable during their lifetime. Implications of equipment failure have become more critical with the increasing size and intricacy of the machinery. Appropriate maintenance planning reduces the overall maintenance cost, increases machine life, and results in optimized life cycle costs. Several techniques have been used in the past to predict reliability, and there’s always been scope for improvement of the same. Researchers are finding new methods for better analysis of faults and reliability from traditional statistical methods to applying artificial intelligence. With the advancement of Industry 4.0, the mining industry is steadily moving towards the predictive maintenance approach to correct potential faults and increase equipment reliability. This paper attempts to provide a comprehensive review of different statistical techniques that have been applied for reliability and fault prediction from both theoretical aspects and industrial applications. Further, the advantages and limitations of the algorithm are discussed, and the efficiency of new ML methods are compared to the traditional methods used.

The objective of this study is to ascertain the essential elements that contribute to the successful implementation of cloud computing in small-scale construction projects, with the ultimate goal of promoting intelligent development in Malaysia. The construction sector is undergoing rapid transformation, and the integration of cloud computing technology can make a substantial contribution to the achievement of project objectives and the promotion of sustainable development. Nonetheless, there exists a dearth of comprehension regarding the function of cloud computing in minor construction undertakings within the Malaysian context. In order to bridge this gap, a mixed-methods approach was implemented, which encompassed a comprehensive review of the literature, interviews with experts, and a preliminary survey that involved 160 participants. Utilizing the findings of the pilot survey, the process of Exploratory Factor Analysis (EFA) was employed to discern and eliminate nonessential determinants of success. A survey utilizing primary questionnaires was conducted with a sample size of 230 participants. The subsequent analysis of 16 critical success factors was carried out through the application of Structural Equation Modeling (SEM). The findings indicate that there are four fundamental constructs that play a crucial role in the effective execution of a project. These include cost, quality, and time management (β = 0.352); planning success (β = 0.360); organizational success (β = 0.351); and communication and coordination (β = 0.299). The research results have favorable ramifications for the construction sector in Malaysia. The integration of cloud computing technology in minor construction endeavors has the potential to augment project efficacy and foster sustainable development. This study offers a roadmap for stakeholders in the construction industry to effectively utilize cloud computing technology for smart development by identifying critical success factors.

Flotation stands out as a successful and extensively employed method for separating valuable mineral particles from waste rock. The efficiency of this process is subjected to the distinct physicochemical attributes exhibited by various minerals. However, the complex combination of multiple sub-processes within flotation presents challenges in controlling this mechanism and achieving optimal efficiency. Consequently, there is a growing dependence on machine learning methods in mineral processing research. This paper provides a comprehensive overview of machine learning and artificial intelligence techniques, presenting their potential applications in flotation processes. The review demonstrates advancements discussed in scholarly research over the past decade and highlights a growing interest in utilizing machine learning methods for monitoring and optimizing flotation processes, as demonstrated by the increasing number of studies in this field. Recent trends also suggest that the course of flotation process monitoring, and control will increasingly focus on the refinement and deployment of deep learning networks developed specifically for froth image extraction and analysis.

The article presents a novel yielding mechanism, especially designed for the rock bolt support. Mechanical rock bolts with an expansion head and equipped with one, two, four and six dome bearing plates were tested in the laboratory conditions. Furthermore, in the Phase2D numerical program, five room and pillar widths were modeled. The main aim of numerical modeling was to determine the maximal range of the rock damage area and the total displacements in the expanded room. The models were made for a room and pillar method with a roof sag for copper ore deposits in the Legnica-Głogów Copper District in Poland. Additionally, in the article a load model of the rock bolt support as a result of a geomechanical seismic event is presented. Based on the results of laboratory tests (load–displacement characteristics), the strain energy of the bolt support equipped with the yielding device in the form of dome bearing plates was determined and compared with the impact energy caused by predicted falling rock layers. Based on the laboratory tests, numerical modeling and mathematical dynamic model of rock bolt support, the dependence of the drop height and the corresponding impact energy for the expanded room was determined.

The article presents methods of making the rock bolt support more yieldable, especially for a stratified roof. Alongside the increasing depth of exploitation of raw material deposits, rock bolt support units are more often designed taking into account more intensive deformations and displacements of underground excavations. In the article, a room and pillar method with mined roof bending and roof reinforcement with bolt patterns of 1 m × 1 m, 1.5 m × 1.5 m and 2 m × 2 m is presented. Moreover, the laboratory tests included 1.8 m long bolts, which were embedded segmentally on the lengths of 100 mm, 150 mm and 200 mm were tested. Based on the load–displacement characteristics, the deformation energy for flat and profiled dome bearing plates was calculated. Making the segmentally embedded resin rock bolt support yieldable enabled it to perform additional work. Furthermore, it was found that rock bolt support with a dome bearing plate took over 2.5 times more energy compared to a rock bolt support equipped with a flat bearing plate.