All countries around the world are blessed with particularly rich cultural heritage. Nowadays, many researchers are exploring different methods for documentation, management, and sustainability of cultural heritage. The aim of this article is to review the state-of-the-art documentation, management, and sustainability techniques in the field of cultural heritage based on the case study in the Museum of King Jan III’s Palace at Wilanów. Various 2D/3D image and range-based methods are discussed demonstrating their applications and drawbacks. The geographical information system (GIS) is presented as a method for management, storage, and maintenance of cultural heritage documentation.

Urban water demand prediction based on climate change is always challenging for water utilities because of the uncertainty that results from a sudden rise in water demand due to stochastic patterns of climatic factors. For this purpose, a novel combined methodology including, firstly, data pre-processing techniques were employed to decompose the time series of water and climatic factors by using empirical mode decomposition and identifying the best model input via tolerance to avoid multi-collinearity. Second, the artificial neural network (ANN) model was optimised by an up-to-date slime mould algorithm (SMA-ANN) to predict the medium term of the stochastic signal of monthly urban water demand. Ten climatic factors over 16 years were used to simulate the stochastic signal of water demand. The results reveal that SMA outperforms a multi-verse optimiser and backtracking search algorithm based on error scale. The performance of the hybrid model SMA-ANN is better than ANN (stand-alone) based on the range of statistical criteria. Generally, this methodology yields accurate results with a coefficient of determination of 0.9 and a mean absolute relative error of 0.001. This study can assist local water managers to efficiently manage the present water system and plan extensions to accommodate the increasing water demand.

Concrete failure will lead to serious safety concerns in the performance of a building structure. It is one of the biggest challenges for engineers to inspect and maintain the quality of concrete throughout the service years in order to prevent structural deterioration. To date, a lot of research is ongoing to develop different instruments to inspect concrete quality. Detection of moisture ingress is important in the structural monitoring of concrete. This paper presents a novel sensing technique using a smart antenna for the non-destructive evaluation of moisture content and deterioration inspection in concrete blocks. Two different standard concrete samples (United Kingdom and Malaysia) were investigated in this research. An electromagnetic (EM) sensor was designed and embedded inside the concrete to detect the moisture content within the structure. In addition, CST microwave studio was used to validate the theoretical model of the EM sensor against the test data. The results demonstrated that the EM sensor at 2.45 GHz is capable of detecting the moisture content in the concrete with linear regression of R2 = 0.9752. Furthermore, identification of different mix ratios of concrete were successfully demonstrated in this paper. In conclusion, the EM sensor is capable of detecting moisture content non-destructively and could be a potential technique for maintenance and quality control of the building performance.

Structural health monitoring (SHM) is an important aspect of the assessment of various structures and infrastructure, which involves inspection, monitoring, and maintenance to support economics, quality of life and sustainability in civil engineering. Currently, research has been conducted in order to develop non-destructive techniques for SHM to extend the lifespan of monitored structures. This paper will review and summarize the recent advancements in non-destructive testing techniques, namely, sweep frequency approach, ground penetrating radar, infrared technique, fiber optics sensors, camera-based methods, laser scanner techniques, acoustic emission and ultrasonic techniques. Although some of the techniques are widely and successfully utilized in civil engineering, there are still challenges that researchers are addressing. One of the common challenges within the techniques is interpretation, analysis and automation of obtained data, which requires highly skilled and specialized experts. Therefore, researchers are investigating and applying artificial intelligence, namely machine learning algorithms to address the challenges. In addition, researchers have combined multiple techniques in order to improve accuracy and acquire additional parameters to enhance the measurement processes. This study mainly focuses on the scope and recent advancements of the Non-destructive Testing (NDT) application for SHM of concrete, masonry, timber and steel structures.

Phosphate in freshwater possesses significant effects on both quality of water and human health. Hence, many treatment methods were used to remove phosphate from water/wastewaters, such as biological and electrochemical methods. Recent researches demonstrated that adsorption approaches are convenient solutions for water/wastewater remediation from phosphate. Thus, the present study employs industrial by-products (bottom ash (BA)), as a cost-effective and eco-friendly alternative, to remediate water from phosphate in the presence of competitor ions (humic acid). This study was initiated by characterising the chemical and physical properties of the BA, sample, then the Central Composite Design (CCD) was utilised to design the required batch experiments and to model the influence of solution temperature (ST), humic acid concentration (HAC), pH of the solution (PoS) and doses of adsorbent (DoA) on the performance of the BA. Langmuir model was utilised to assess the adsorption process. The outcomes of this study evidenced that the BA removed 83.8% of 5.0 mg/l of phosphates at ST, HAC, PoS and DoA 35 °C, 20 mg/L, 5 and 55 g/L, respectively. The isotherm study indicated a good affinity between BA and phosphate. Additionally, the developed model, using the CCD, reliably simulated the removal of phosphates using BA (R2 = 0.99).