Development of Engineered Cementitious Composites with Conductive Inclusions for Use in Self-sensing Applications

The mechanical and a.c. electrical properties of a new varietal of engineered cementitious composite (ECC) incorporating conductive inclusions are presented. Electrical measurements were undertaken over a wide frequency range while curing and when under uniaxial tensile loading to study the influence of ongoing hydration and multiple microcrack formation on the composite electrical impedance. When presented in Nyquist format, the work shows that conductive inclusions reduce the bulk resistance of the composite while enhancing its polarizability, transforming the classic, single-arc bulk response of typical cement-based materials to a two-arc response. The bulk resistance was shown to increase with time and damage, the former being due to refinement of pore-structure during hydration. Conductive inclusions smaller than the average microcrack width of ECC were shown to improve the sensitivity of the composite to cracking, while those with high aspect ratio resulted in better electrical continuity at low volume fractions

Investigating the self-monitoring potentials of an engineered cementitious composite

Cement-based materials are an important group of structural materials, and the ability of such materials to respond to internal and external changes could provide an added feature which could further enhance their range of application. One area of development that has received increasing attention within the research community is making use of the self-monitoring features of concrete with respect to deformation and damage. Ordinary concrete is, however, a poor conductor of electricity, particularly after cracking and under dry conditions, and attention is therefore directed towards a highly damage-tolerant family of concrete types with superior tensile strain capacities and controllable crack widths, generally known as the Engineered Cementitious Composite (ECC). This thesis explores the self-monitoring capabilities of the ECC under mechanical and non-mechanical loading and presents the a.c. electrical properties of ECC over the frequency range 1 Hz–10 MHz. The project was developed on three general fronts, focusing on key factors affecting the electrical properties of ECC: (i) investigation of the influence of cement hydration and temperature; (ii) evaluation of the influence of tensile straining and cracking; and (iii) investigation of the influence of wetting and drying. Laboratory samples of different geometries were fabricated and tested under various curing regimes and test conditions. Results are presented from each of the sub-themes listed above, with measured data presented in a range of formats to provide insights into features that could potentially be exploited for self-monitoring. This includes the Nyquist format, which has been generally used in a.c. electrical property measurements, and the permittivity and conductivity, which were de-embedded from the measured impedance and presented in the frequency domain to elucidate the nature of the conduction and polarization processes. Equivalent circuit models were also developed to simulate the measured response and offer a phenomenological interpretation of the origin of some of the features observed in the electrical response. It was found that, over a curing period of 180 days, the ECC displayed a classic impedance response comprising an electrode spur, a weak intermediate "plateau" region and a single bulk arc. Both conductivity and relative permittivity were found to be frequency dependent due to bulk relaxation processes operating within the composite. It was found that cement hydration has a negligible effect on the relative permittivity at high frequencies (i.e., > 1 MHz), as evidenced by the merging of the relative permittivity at different curing ages when presented in a logarithmic format. Moreover, the knowledge regarding the temperature effects on the electrical properties (through the activation energy approach) will have direct practical significance for removing the effect of natural temperature fluctuations. Tensile straining was shown to result in a detectable change in the impedance response but retained a similar overall profile. When presented in the frequency domain, a downward displacement in relative permittivity at high frequency (i.e., 1 MHz) was evident with increasing tensile strain for ECC with average crack widths in the range 50 μm–65 μm. In the ECC with larger average crack widths (i.e., >100 μm), a downward displacement in relative permittivity profiles together with an enhancement of the relative permittivity within the frequency range (> 10 kHz to ~low MHz) was observed. Overall, it is shown that the relative permittivity at the high-frequency end could be exploited as a potentially useful indicator for strain/damage detection. The electrical properties of ECC display significant increases in the impedance response when the material is subjected to drying. When presented in the frequency domain, an enhancement of the relative permittivity within the frequency range > 1 kHz to ~low MHz was observed. Within the low-frequencies range of ~1Hz to < 1 kHz, the relative permittivity of the un-cracked ECC curves showed a slight decrease, while the cracked-ECC was sensitive to drying. When subjected to wetting, a reduction of the impedance response was observed, and the enhancement of the relative permittivity at high frequencies disappeared, due to the presence of water in the micro-cracks. This thesis demonstrates the use of multi-frequency measurements to characterise the electrical properties of ECC under mechanical and non-mechanical loading.Engineering and Physical Sciences Research Council, U.K. (Grant EP/N028597/1)

Investigating and Ranking Labor Productivity Factors in the Egyptian Construction Industry

This article sets out to investigate and rank the factors that affect labor productivity in the Egyptian construction industry. To achieve this, a quantitative research methodology is adopted and it entails the use of structured survey questionnaires. The data obtained was analysed using the relative importance index. The results from the analysis revealed ten major factors affecting labor productivity in the construction industry in Egypt. These factors are: tools and equipment shortages; delay in material delivery on site; payment delay; undisciplined labor; material shortage; rework; labor expensive and skills; low quality of raw material; waiting for equipment to arrive; and on-site accident. Consequently, these findings would serve as a useful tool and a basis to make recommendations to governmental and construction personnel regarding the productivity of labor in the Egyptian construction industry

Personal Protective Equipment Detection: A Deep-Learning-Based Sustainable Approach

Personal protective equipment (PPE) can increase the safety of the worker for sure by reducing the probability and severity of injury or fatal incidents at construction, chemical, and hazardous sites. PPE is widely required to offer a satisfiable safety level not only for protection against the accidents at the aforementioned sites but also for chemical hazards. However, for several reasons or negligence, workers may not commit to and comply with the regulations of wearing the equipment, occasionally. Since manual monitoring is laborious and erroneous, the situation demands the development of intelligent monitoring systems to offer the automated real-time and accurate detection of PPE compliance. As a solution, in this study, Deep Learning and Computer Vision are investigated to offer near real-time and accurate PPE detection. The four colored hardhats, vest, safety glass (CHVG) dataset was utilized to train and evaluate the performance of the proposed model. It is noteworthy that the solution can detect eight variate classes of the PPE, namely red, blue, white, yellow helmets, head, person, vest, and glass. A two-stage detector based on the Fast-Region-based Convolutional Neural Network (RCNN) was trained on 1699 annotated images. The proposed model accomplished an acceptable mean average precision (mAP) of 96% in contrast to the state-of-the-art studies in literature. The proposed study is a potential contribution towards the avoidance and prevention of fatal/non-fatal industrial incidents by means of PPE detection in real-time