Rapid visual screening is a quick and simple approach often used by researchers to estimate the seismic vulnerability of buildings in an area. In this study, preliminary seismic vulnerability assessment of 500 buildings situated at Northern and Eastern George Town, Malaysia, was carried out by utilizing a modified FEMA-154 (2002) method that suits Malaysian conditions. Data were collected from online sources via Google Maps and Google Earth instead of traditional surveying data collection through street screening. The seismic assessment analysis of this study was based on the RVS performance score and the damage state classification for each building typology. This approach generates, for each building, a final performance score based on governing parameters such as structural resisting system, height, structural irregularities, building age, and soil type. The findings revealed the immediate need for effective seismic mitigation strategies, as 90% of the studied buildings required a further detailed analyses to pinpoint their exact seismic vulnerability performance. Most of the surveyed buildings were predicted to experience moderate-to-substantial damage, with 220 out of 500 being classed as damage state 2 (D2) and damage state 3 (D3). A GIS map, “RVS Malaysian Form-George Town Area”, was generated via ArcGIS and shared with the public to provide vital information for further research.

Precast concrete sandwich panels (PCSPs) are utilized for the external cladding of structures (i.e., residential, and commercial) due to their high thermal efficiency and adequate composite action that resist applied loads. PCSPs are composed of an insulating layer with high thermal resistance that is mechanically connected to the concrete. In the recent decades, PCSPs have been a viable alternative for the fast deployment of structures due to the low fabrication and maintenance cost. Furthermore, the construction of light and thin concrete wythes that can transfer and resist shear loads has been achieved with the utilization of high-performance cementitious composites. As a result, engineers prefer PCSPs for building construction. PCSP design and use have been examined to guarantee that a building is energy efficient, has structural integrity, is sustainable, is comfortable, and is safe. Hence, this paper reviews the expanding knowledge regarding the current development of the mechanical and thermal properties of the PCSPs components; subsequently, future potential research directions are suggested.

Transmission towers are a vital lifeline for modern living and are crucial structures that must remain operational even after a seismic event. However, the towers are largely designed to withstand the effects of wind alone and not earthquakes, and the seismic influences on tower design and construction have hitherto been ignored. The purpose of this study was to evaluate the seismic performance of a latticed steel transmission tower-line system that is subjected to a variety of seismic situations (Far-Field, Near-Field and Repeated Earthquakes) using probabilistic vulnerability functions and Collapse Margin Ratios in accordance with FEMA-P695. Nonlinear Time History Analyses were performed by incorporating an array of 36 strong ground motions to develop the Incremental Dynamic Analysis and to generate the fragility functions for three performance limit states as referenced in FEMA 356. The results showed that the single event seismic performance of the tower is better than its performance after multiple ground motions owing to aftershock impact, while near-field excitations led to greater susceptibility and fragility than far-field scenarios. Thus, near-field ground motion is more harmful to the tower and could result in its failure or collapse with only a small reduction in damage relative to the impact of the aftershock.

Reservoir inflow (Q_flow) forecasting is one of the crucial processes in achieving the best water resources management in a particular catchment area. Although physical models have taken place in solving this problem, those models showed a noticeable limitation due to their requirements for huge efforts, hydrology and climate data, and time-consuming learning process. Hence, the recent alternative technology is the development of the machine learning models and deep learning neural network (DLNN) is the recent promising methodology explored in the field of water resources. The current research was adopted to forecast Q_flow at two different catchment areas characterized with different type of inflow stochasticity, (semi-arid and topical). Validation against two classical algorithms of neural network including multilayer perceptron neural network (MLPNN) and radial basis function neural network (RBFNN) was elaborated and discussed. The research was further investigated the potential of the feature selection algorithm “genetic algorithm (GA)”, for identifying the appropriate predictors. The research finding confirmed the feasibility of the developed DLNN model for the investigated two case studies. In addition, the DLNN model confirmed its capability in solving daily scale Q more accurately in comparison with the monthly scale. The applied GA as feature selection algorithm was reduced the dimension and complexity of the learning process of the applied predictive model. Further, the research finding approved the adequacy of the data span used in the current investigation development of computerized ML algorithm.