Risk and uncertainty have continuously troubled the construction industry compared to other industries due to its complexity, magnitude and time consuming characteristic. As the process of risk management involves predicting the unpredictable, it can be expressed as the most vital management tool to cope with project uncertainties. Risk management can be treated as an essential element for creating value to a project and improving project performance in terms of cost, time and quality. However, systematic risk management is not implemented in most of construction companies in Malaysia. Consequently, this situation can ultimately lead to project failure in terms of cost overruns, schedule delays and poor quality performances. Therefore, this research aims to investigate the current practice of risk management in the Malaysian construction industry and attempts to assess the process and various tools/techniques currently used and applied to handle the projects. The data have been obtained through a series of semi-structures interviews from industrial practitioners. Findings conclude that the level of risk management practices in Malaysian construction companies are relatively low and lacks in knowledge on the subject. In addition, only simple tools and techniques are used to identify, analyze, respond, and monitor the risks. Furthermore, the frequency of use of these tools is also found to be very low. Possible cooperation between the academia and industry might improve risk management practice in the Malaysia construction industry. 

Ag-doped polymer (polyethylene oxide: PEO) conductive-bridging-random-access-memory (CBRAM) cell using inert Pt electrodes is a potential electro-forming free CBRAM cells in which electro-forming and electro-breaking of nanoscale (16~22-nm in diameter) conical or cylindrical Ag filaments occurs after a set or reset bias is applied. The dependency of the morphologies of the Ag filaments in the PEO polymer electrolyte indicates that the electro-formed Ag filaments bridging the Pt cathode and anode are generated by Ag+ ions drifting in the PEO polymer electrolyte toward the Pt anode and that Ag dendrites grow via a reduction process from the Pt anode, whereas electro-breaking of Ag filaments occurs through the oxidation of Ag atoms in the secondary dendrites and the drift of Ag+ ions toward the Pt cathode. The Ag doping concentration in the PEO polymer electrolyte determines the bipolar switching characteristics; i.e., the set voltage slightly decreases, while the reset voltage and memory margin greatly increases with the Ag doping concentration.

The prediction of settlement during tunneling presents multiple challenges, as such settlement is governed by not only the local geology but also construction methods and practices, such as tunnel boring machine (TBM). To avoid undesirable settlement, engineers must predict the settlement under given conditions. The widely used methods are analytical solutions, empirical solutions, and numerical solutions. Analytical or empirical solutions, however, have limitations, which cannot incorporate the major causes of subsidence, such as unexpected geological conditions and TBM operational issues, among which cutterhead pressure and thrust force-related factors are the most influential. In settlement prediction, to utilize the machine data of TBM, two phases of long short-term memory (LSTM) models are devised. The first LSTM model is designed to capture the features affecting surface settlement. The second model is for the prediction of subsidence against the extracted features. One thing to note is that predicted subsidence is the evolution of settlement along TBM drive rather than its maximum value. The proposed deep-learning models are capable of predicting the subsidence of training and test sets with excellent accuracy, anticipating that it could be an effective tool for real-world tunneling and other underground construction projects.

This paper presents a complete three-dimensional stress determination using hydraulic fracturing data from three inclined boreholes, drilled from the floor of an underground cavern at a depth of about 100 m. Both conventional hydraulic fracturing (HF) and hydraulic testing of pre-existing fractures (HTPF) were carried out at all test points to acquire reliable data and conduct the integrated stress

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